Signal processing for continuous analyte sensor

ABSTRACT

Systems and methods for dynamically and intelligently estimating analyte data from a continuous analyte sensor, including receiving a data stream, selecting one of a plurality of algorithms, and employing the selected algorithm to estimate analyte values. Additional data processing includes evaluating the selected estimative algorithms, analyzing a variation of the estimated analyte values based on statistical, clinical, or physiological parameters, comparing the estimated analyte values with corresponding measure analyte values, and providing output to a user. Estimation can be used to compensate for time lag, match sensor data with corresponding reference data, warn of upcoming clinical risk, replace erroneous sensor data signals, and provide more timely analyte information encourage proactive behavior and preempt clinical risk.

RELATED APPLICATION

This application is a division of U.S. application Ser. No. 11/007,920,filed Dec. 8, 2004, which claims the benefit of U.S. ProvisionalApplication No. 60/528,382, filed Dec. 9, 2003, U.S. ProvisionalApplication No. 60/587,787, filed Jul. 13, 2004, and U.S. ProvisionalApplication No. 60/614,683, filed Sep. 30, 2004. Each above-referencedapplication is hereby incorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods formeasuring and analyzing data obtained from a continuous analyte sensor.More particularly, the present invention relates to dynamic andintelligent estimation of analyte values from a continuous analytesensor.

BACKGROUND OF THE INVENTION

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which may cause anarray of physiological derangements (for example, kidney failure, skinulcers, or bleeding into the vitreous of the eye) associated with thedeterioration of small blood vessels. A hypoglycemic reaction (low bloodsugar) may be induced by an inadvertent overdose of insulin, or after anormal dose of insulin or glucose-lowering agent accompanied byextraordinary exercise or insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically comprises uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a personwith diabetes will normally only measure his or her glucose levels twoto four times per day. Unfortunately, these time intervals are so farapart that the person with diabetes will likely find out too late,sometimes incurring dangerous side effects, of a hyper- or hypo-glycemiccondition. In fact, it is not only unlikely that a person with diabeteswill take a timely SMBG value, but the person with diabetes will notknow if their blood glucose value is going up (higher) or down (lower)based on conventional methods, inhibiting their ability to make educatedinsulin therapy decisions.

Some attempts have been made to continuously measure the glucoseconcentration in a person with diabetes. Typically, these continuousglucose sensors have required a reference glucose monitor (for example,SMBG) to provide reference glucose values in order to calibrate and/orinterpret data from the continuous glucose monitor. While the use ofthese reference glucose values can be helpful, they can also causenumerous inconsistencies and instabilities in the data output of thecontinuous glucose sensor. As one example, a time lag can be caused byan interstitial fluid sample measured by an implantable glucose sensoras compared with a blood sample measured by an external referenceglucose monitor, which can cause inaccurate calibration, outlierdetection, and data output. Additionally, the static use of algorithmsmay not adequately represent physiological trends in a human, forexample.

SUMMARY OF THE INVENTION

There exists a need for improvements in data processing of continuousglucose sensors in order to better handle the inconsistencies andinstabilities that occur in glucose measurements and associated dataanalysis.

Accordingly, in a first embodiment, a method for estimating an analytevalue from a continuous analyte sensor is provided, the methodcomprising receiving a data stream from the continuous analyte sensorfor a first time period, thereby obtaining a measured analyte value;estimating at least one analyte value for a second time period based onthe data stream; and comparing the estimated analyte value with themeasured analyte value.

In an aspect of the first embodiment, the step of receiving a datastream comprises receiving a data stream that has been algorithmicallysmoothed.

In an aspect of the first embodiment, the step of receiving the datastream comprises receiving a raw data stream.

In an aspect of the first embodiment, the step of estimating at leastone analyte value further comprises selecting an algorithm from aplurality of algorithms based on an analysis of the data stream prior toestimating at least one analyte value.

In an aspect of the first embodiment, the step of selecting an algorithmis conditional upon at least one value selected from the groupconsisting of analyte concentration, rate of change, acceleration, andan individual historical pattern of the data stream.

In an aspect of the first embodiment, the step of selecting an algorithmcomprises employing the plurality of algorithms on the data stream anddetermining which of the plurality of algorithms best correlates withthe data stream.

In an aspect of the first embodiment, the algorithm is selected from thegroup consisting of polynomial regression, autoregressive algorithms,Fourier transform, Wavelet transform, neural network-based mapping,fuzzy logic based pattern matching, and Genetic-Algorithms based patternmatching.

In an aspect of the first embodiment, the step of selecting an algorithmfurther comprises applying a physiological boundary to the selectedalgorithm.

In an aspect of the first embodiment, the method further comprisesevaluating the selected algorithm by applying an evaluation functionprior to employing the selected algorithm to estimate the analyte value,wherein the evaluation function is selected from the group consisting ofa data association function, a curvature formula, and a physiologicalboundary.

In an aspect of the first embodiment, the step of analyzing a variationcomprises analyzing a variation of the estimated analyte value based ona parameter selected from a statistical parameter, a clinical parameter,or a physiological parameter.

In an aspect of the first embodiment, the step of analyzing a variationcomprises determining a physiological variation from the estimatedanalyte value.

In an aspect of the first embodiment, the step of analyzing a variationcomprises determining a statistical variation from the estimated analytevalue based on a statistical parameter.

In an aspect of the first embodiment, the step of comparing comprisesdetermining a deviation between the estimated analyte value and themeasured analyte value.

In an aspect of the first embodiment, the step of analyzing a variationcomprises analyzing a variation of the estimated analyte value based onthe deviation determined by the step of comparing.

In an aspect of the first embodiment, the method further comprises astep of recognizing a pattern by monitoring a physiological parameterover time.

In an aspect of the first embodiment, the step of analyzing a variationcomprises analyzing a variation in the physiological parameter todetermine a variation of the estimated analyte value.

In an aspect of the first embodiment, the step of analyzing a variationcomprises determining a variation of the estimated analyte value basedon a clinical risk of the estimated analyte value to the user.

In an aspect of the first embodiment, the method further comprisesproviding output based on the estimated analyte data.

In an aspect of the first embodiment, the output displays an estimatedanalyte value to the user.

In an aspect of the first embodiment, the output displays an estimatedpath of analyte values for a future time period. In an aspect of thefirst embodiment, the output displays an estimated analyte value at afuture point in time.

In an aspect of the first embodiment the output displays an estimatedfuture time period of clinical risk.

In an aspect of the first embodiment, the time period of clinical riskis determined when an estimated analyte value falls outside of a normalanalyte threshold.

In an aspect of the first embodiment, the analyte is blood glucose andthe normal analyte threshold is from about 100 mg/dL to about 160 mg/dL.

In an aspect of the first embodiment, the normal analyte threshold isfrom about 80 mg/dL to about 200 mg/dL.

In an aspect of the first embodiment, the normal analyte threshold isfrom about 55 mg/dL to about 220 mg/dL.

In an aspect of the first embodiment, the output displays at least oneclinically acceptable target analyte value.

In an aspect of the first embodiment, the target analyte value iscustomizable by the user.

In an aspect of the first embodiment, the target analyte value is basedon an individual physiological pattern.

In an aspect of the first embodiment, a parameter utilized indetermining a therapy recommendation is customizable by the user.

In an aspect of the first embodiment, the output comprises an icon thathas a shape representative of the analyzed variation of the estimatedanalyte value.

In an aspect of the first embodiment, the output comprises a dynamicvisual representation of the analyzed variation of estimated analytevalue.

In an aspect of the first embodiment, the output prompts the user toobtain a reference analyte value.

In an aspect of the first embodiment, the output provides at least onealarm selected from the group consisting of a visual alarm, an audiblealarm, and a tactile alarm, wherein the alarm is provided based on aclinical risk associated with the estimated analyte value.

In an aspect of the first embodiment, the alarm is based on at least oneparameter selected from the group consisting of an analyte value, a rateof change, acceleration of a rate of change, and an individualphysiological pattern.

In an aspect of the first embodiment, the output continuously providesestimated analyte values.

In an aspect of the first embodiment, the output selectively providesthe estimated analyte value based on an event trigger.

In an aspect of the first embodiment, the output provides a clinicalrisk zone that is displayed on a screen.

In an aspect of the first embodiment the clinical risk zone comprises atleast one of a shaded region, a colored region, and a patterned region.

In an aspect of the first embodiment, the clinical risk zone is boundedby a threshold.

In an aspect of the first embodiment, the output provides the estimatedanalyte value and the variation of the estimated analyte value on atrend graph.

In an aspect of the first embodiment, the output provides the estimatedanalyte value and the variation of the estimated analyte value on agradient bar.

In an aspect of the first embodiment, the output is sent to a personalcomputer.

In an aspect of the first embodiment, the output is sent to a modem.

In an aspect of the first embodiment, the output is sent to an insulinpen.

In an aspect of the first embodiment, the output is sent to an insulinpump.

In a second embodiment, a method for estimating an analyte value from acontinuous analyte sensor is provided, the method comprising receiving adata stream from the continuous analyte sensor for a time period;estimating at least one analyte value for a future time based on thedata stream; analyzing a variation of the estimated analyte value basedon a parameter selected from the group consisting of a statisticalparameter, a clinical parameter, or a physiological parameter; andproviding an output based on the estimated analyte value and thevariation of the estimated analyte value.

In an aspect of the second embodiment, the method further comprisesevaluating the selected algorithm by applying an evaluation functionprior to employing the selected algorithm to estimate the analyte value,wherein the evaluation function is selected from the group consisting ofa data association function, a curvature formula, and a physiologicalboundary.

In a third embodiment, a method for estimating an analyte value from acontinuous analyte sensor is provided, the method comprising receiving adata stream from the continuous analyte sensor for a time period;selecting at least one algorithm from a plurality of algorithms based onan analysis of the data stream; evaluating the algorithm based on atleast one parameter selected from the group consisting of a statisticalparameter, a physiological parameter, and a clinical parameter; andemploying the selected algorithm based on the step of evaluating toestimate at least one analyte value.

In an aspect of the third embodiment, the method further comprisesevaluating the selected algorithm by applying an evaluation functionprior to employing the selected algorithm to estimate the analyte value,wherein the evaluation function is selected from the group consisting ofa data association function, a curvature formula, and a physiologicalboundary.

In an aspect of the third embodiment, the method further comprisesanalyzing a variation of the estimated analyte value based on aparameter selected from a statistical parameter, a clinical parameter,and a physiological parameter.

In an aspect of the third embodiment, the output further comprises atherapy recommendation to help the user obtain a target analyte value.

In a fourth embodiment, a method for matching a data pair using datafrom a continuous analyte sensor with data from a reference analytesource is provided, the method comprising receiving a data stream fromthe continuous analyte sensor, the data comprising at least one sensordata point; receiving reference data from a reference analyte monitor,the data comprising at least one reference data point; estimating atleast one analyte value for a time period during which no data existsbased on the data stream; and creating at least one matched data pair bymatching the reference data to the analyte value.

In an aspect of the fourth embodiment, the step of receiving the datastream comprises receiving a data stream that has been algorithmicallysmoothed.

In an aspect of the fourth embodiment, the step of receiving referencedata comprises downloading reference data via a cabled connection.

In an aspect of the fourth embodiment, the step of receiving referencedata comprises downloading reference data via a wireless connection.

In an aspect of the fourth embodiment, the step of receiving referencedata from a reference analyte monitor comprises receiving within areceiver an internal communication from a reference analyte monitorintegral with the receiver.

In an aspect of the fourth embodiment, the algorithm is selected fromthe group consisting of polynomial regression, autoregressivealgorithms, Fourier transform, Wavelet transform, neural network-basedmapping, fuzzy logic based pattern matching, and Genetic-Algorithmmatching.

In an aspect of the fourth embodiment, the method further comprisesevaluating the selected algorithm by applying an evaluation functionprior to employing the selected algorithm to estimate the analyte value,wherein the evaluation function is selected from the group consisting ofa data association function, a curvature formula, and a physiologicalboundary.

In an aspect of the fourth embodiment, the method further comprisescomparing the estimated analyte value with the corresponding measuredanalyte value to determine a time lag between the estimated analytevalue and the corresponding measured analyte value.

In a fifth embodiment, a method for compensating for a time lag ofcontinuous analyte sensor data by estimating an analyte value for apresent time from which the continuous analyte sensor data is delayed isprovided, the method comprising receiving a data stream from thecontinuous analyte sensor, wherein the data stream comprises aphysiological time lag from the present time or a computational time lagfrom the present time; continuously estimating or periodicallyestimating analyte values for a present time period based on the datastream to compensate for the physiological time lag or the computationaltime lag in the analyte sensor data; and continuously providing orperiodically providing an output to a user based on the estimatedanalyte values, such that the output of the estimated analyte valuesprovides present time analyte values to the user.

In an aspect of the fifth embodiment, the analyte value estimation stepfurther comprises selecting an algorithm from a plurality of algorithmsbased on analysis of the data stream prior to estimating the analytevalues.

In an aspect of the fifth embodiment, the algorithm selection isconditional upon at least one value selected from the group consistingof analyte concentration, rate of change, acceleration, and anindividual historical pattern of the data stream.

In an aspect of the fifth embodiment, the method further comprisesevaluating the selected algorithm by applying a data associationfunction, a curvature formula, or physiological boundaries prior toemploying the selected algorithm to estimate analyte values.

In an aspect of the fifth embodiment, the method further comprisesanalyzing a variation of the estimated analyte values based onparameters selected from the group consisting of statistical parameters,clinical parameters, and physiological parameters.

In an aspect of the fifth embodiment, the step of analyzing a variationcomprises determining a physiological variation from estimated analytevalues.

In an aspect of the fifth embodiment, the step of analyzing a variationcomprises determining a statistical variation from the estimated analytevalues based on a statistical parameter.

In an aspect of the fifth embodiment, the method further comprisescomparing the estimated analyte values with the measured analyte valuesto determine a deviation between the estimated analyte values and themeasured analyte values.

In an aspect of the fifth embodiment, the step of analyzing a variationcomprises analyzing a variation of the estimated analyte values based onthe deviation determined by the step of comparing.

In an aspect of the fifth embodiment, the output displays estimatedanalyte values to the user.

In an aspect of the fifth embodiment, the output further comprises atherapy recommendation to help the user obtain a target analyte value.

In an aspect of the fifth embodiment, the output comprises an icon thathas a shape representative of the analyzed variation of the estimatedanalyte value.

In a sixth embodiment, a method for estimating analyte values from acontinuous analyte sensor is provided, the method comprising receiving adata stream from the continuous analyte sensor for a time period; andestimating one or more analyte values for a time period based on thedata stream, wherein analyte estimation comprises performing analgorithm to estimate analyte values and applying physiologicalboundaries to the estimated analyte values.

In an aspect of the sixth embodiment, the analyte value estimation stepfurther comprises selecting an algorithm from a plurality of algorithmsbased on analysis of the data stream prior to estimating the analytevalues.

In an aspect of the sixth embodiment, the algorithm selection isconditional upon at least one of analyte concentration, rate of change,acceleration, and individual historical patterns of the data stream.

In an aspect of the sixth embodiment, the algorithm is selected from thegroup consisting of polynomial regression, autoregressive algorithms,Fourier transform, Wavelet transform, neural network-based mapping,fuzzy logic based pattern matching, and Genetic-Algorithm matching.

In an aspect of the sixth embodiment, the method further comprisesevaluating the selected algorithm by applying a data associationfunction, a curvature formula, or physiological boundaries prior toemploying the selected algorithm to estimate analyte values.

In an aspect of the sixth embodiment, the method further comprisesanalyzing a variation of the estimated analyte values based onstatistical, clinical, or physiological parameters.

In an aspect of the sixth embodiment, the time period of clinical riskis determined when an estimated analyte value falls outside of a normalanalyte threshold.

In an aspect of the sixth embodiment, the output selectively providesestimated analytes value based on an event trigger.

In a seventh embodiment, a method for displaying analyte data from acontinuous analyte sensor is provided, the method comprising receiving adata stream from the continuous analyte sensor for a time period;calibrating the data stream using a conversion function to determine atleast one calibrated analyte value; analyzing a variation of at leastone calibrated analyte value based on a parameter selected from thegroup consisting of a statistical parameter, a clinical parameter, and aphysiological parameter; and providing an output based on the calibratedanalyte value and the variation of the calibrated analyte value.

In an aspect of the seventh embodiment, the output comprises a numericalrepresentation of a calibrated analyte value and a variation of thecalibrated analyte value.

In an aspect of the seventh embodiment, the output comprises a numericalrepresentation of a range of possible analyte values.

In an aspect of the seventh embodiment, the output further comprises anarrow representing a rate of change of the calibrated analyte values.

In an eighth embodiment, a system for estimating analyte data from acontinuous analyte sensor is provided, the system comprising an inputmodule operatively connected to the continuous analyte sensor thatreceives a data stream comprising a plurality of time spaced sensor datapoints from the analyte sensor; and a processor module comprisingprogramming that estimates at least one analyte value for a time periodbased on the data stream and compares the estimated analyte value with acorresponding measured analyte value.

In an aspect of the eighth embodiment, the input module is adapted toreceive a data stream that has been algorithmically smoothed.

In an aspect of the eighth embodiment, the input module is adapted toreceive a raw data stream.

In an aspect of the eighth embodiment, the programming to estimate ananalyte value further comprises programming to select an algorithm froma plurality of algorithms based on analysis of the data stream prior toestimating the analyte value.

In an aspect of the eighth embodiment, the processor module furthercomprises programming to select the algorithm conditional upon at leastone parameter selected from the group consisting of an analyteconcentration, a rate of change, acceleration of a rate of change, andan individual historical pattern of the data stream.

In an aspect of the eighth embodiment, the processor module runs theplurality of algorithms on the data stream and determines which of theplurality of algorithms best correlates with the data stream.

In an aspect of the eighth embodiment, the processor module comprisesprogramming to apply a physiological boundary to the selected algorithm.

In an aspect of the eighth embodiment, the processor module furthercomprises programming to evaluate the selected algorithm by applying adata association function, a curvature formula, or physiologicalboundaries prior to employing the selected algorithm to estimate analytevalues.

In an aspect of the eighth embodiment, the processor module furthercomprises programming to analyze a variation of the estimated analytevalue based on a parameter selected from the group consisting ofstatistical parameters, clinical parameters, and physiologicalparameters.

In an aspect of the eighth embodiment, the programming to analyze avariation comprises determining a physiological variation from theestimated analyte value.

In an aspect of the eighth embodiment, the programming to analyze avariation comprises determining statistical variation from the estimatedanalyte value based on statistical parameters.

In an aspect of the eighth embodiment, the programming to compare theestimated analyte value with the measured analyte value furthercomprises determining a deviation between the estimated analyte valueand the measured analyte value.

In an aspect of the eighth embodiment, the programming to analyze avariation comprising analyzing a variation of the estimated analytevalues based on the deviation determined by the comparison step.

In an aspect of the eighth embodiment, the processor module furthercomprises programming to recognize a pattern by monitoring aphysiological pattern over time.

In an aspect of the eighth embodiment, the programming to analyze avariation comprises analyzing the physiological pattern to determine avariation of the estimated analyte value.

In an aspect of the eighth embodiment, the programming to analyze avariation comprises determining the variation of the estimated analytevalue based on a clinical risk of the estimated analyte value to theuser.

In an aspect of the eighth embodiment, the system further comprises anoutput module comprising programming to output data based on theestimated analyte data.

In a ninth embodiment, a system for estimating analyte values from acontinuous analyte sensor, the system comprising an input moduleoperatively connected to the continuous analyte sensor that receives adata stream comprising a plurality of time spaced sensor data pointsfrom the analyte sensor; a processor module comprising programming thatestimates at least one analyte value for a future time based on the datastream, and analyzes a variation of the estimated analyte value based ona parameter selected from the group consisting of a statisticalparameter, a clinical parameter, and a physiological parameter; and anoutput module associated with the processor module and comprisingprogramming to output analyte data based on at least one estimatedanalyte value and the variation of the estimated analyte value.

In an aspect of the ninth embodiment, the input module is adapted toreceive a data stream that has been algorithmically smoothed.

In an aspect of the ninth embodiment, the input module is adapted toreceive a raw data stream.

In an aspect of the ninth embodiment, the programming to estimate ananalyte value further comprises programming to select an algorithm froma plurality of algorithms based on analysis of the data stream prior toestimating the analyte value.

In an aspect of the ninth embodiment, the programming to select analgorithm is conditional upon at least one parameter selected from thegroup consisting of an analyte concentration, a rate of change, anacceleration of a rate of change, and an individual historical patternof the data stream.

In an aspect of the ninth embodiment, the programming to select analgorithm further comprises employing the plurality of algorithms on thedata stream and determining which of the plurality of algorithms bestcorrelates with the data stream.

In an aspect of the ninth embodiment, the algorithms are selected fromthe group consisting of polynomial regression, autoregressivealgorithms, Fourier transform, Wavelet transform, neural network-basedmapping, fuzzy logic based pattern matching, and Genetic-Algorithmmatching.

In an aspect of the ninth embodiment, the processor module furthercomprises programming to apply a physiological boundary to the selectedalgorithm.

In an aspect of the ninth embodiment, the processor module furthercomprises programming to evaluate the selected algorithm by applying adata association function, a curvature formula, or physiologicalboundaries prior to employing the selected algorithm to estimate theanalyte value.

In an aspect of the ninth embodiment, the programming to analyze avariation comprises determining a physiological variation from theestimated analyte value.

In an aspect of the ninth embodiment, the programming to analyze avariation comprises determining statistical variation from the estimatedanalyte value based on a statistical parameter.

In an aspect of the ninth embodiment, the processor module compares theestimated analyte value with the measured analyte value to determine adeviation between the estimated analyte value and the measured analytevalue.

In an aspect of the ninth embodiment, the programming to analyze avariation comprises analyzing a variation of the estimated analyte valuebased on the deviation.

In an aspect of the ninth embodiment, the processor module furthercomprises programming to recognize a pattern by monitoring aphysiological pattern over time.

In an aspect of the ninth embodiment, the programming to analyze avariation comprises analyzing the physiological pattern to determine avariation of the estimated analyte value.

In an aspect of the ninth embodiment, the programming to analyze avariation comprises determining the variation of the estimated analytevalue based the clinical risk of the estimated analyte value to theuser.

In an aspect of the ninth embodiment, the output displays estimatedanalyte value to the user.

In a tenth embodiment, a system for estimating analyte values from acontinuous analyte sensor is provided, the system comprising an inputmodule operatively connected to the continuous analyte sensor thatreceives a data stream comprising a plurality of time spaced sensor datapoints from the analyte sensor; and a processor module comprisingprogramming that selects an algorithm from a plurality of algorithmsbased on an analysis of the data stream, that evaluates the algorithmbased on a parameter selected from the group consisting of statisticalparameters, physiological parameters, and clinical parameters, and thatemploys a selected algorithm based on the algorithm evaluation toestimate at least one analyte value.

In an eleventh embodiment, a system for matching data pairs from acontinuous analyte sensor with data from a reference analyte source, thesystem comprising a sensor input module operatively connected to thecontinuous analyte sensor that receives a data stream comprising aplurality of time spaced sensor data points from the analyte sensor; areference input module receiving reference data from a reference analytemonitor, including at least one reference data point; and a processormodule comprising programming that estimates at least one analyte valuefor a time period during which no data exists based on the data streamand creates at least one matched data pair by matching reference analytedata to the estimated analyte value.

In a twelfth embodiment, a system for compensating for a time lag ofcontinuous analyte sensor data by estimating an analyte value for apresent time from which the continuous analyte sensor data is delayed isprovided, the system comprising an input module operatively connected tothe continuous analyte sensor that receives a data stream comprising aplurality of time spaced sensor data points from the analyte sensor; aprocessor module comprising programming that continuously estimates orperiodically estimates analyte values for the present time period basedon the data stream to compensate for the physiological or computationaltime lag in the analyte sensor data; and an output module associatedwith the processor module and comprising programming to continuouslyprovide or periodically provide an output to the user based on theestimated analyte values, such that output of the estimated analytevalues provides present time analyte values to the user.

In a thirteenth embodiment, a system for estimating analyte values froma continuous analyte sensor is provided, the system comprising an inputmodule operatively connected to the continuous analyte sensor thatreceives a data stream comprising a plurality of time spaced sensor datapoints from the analyte sensor; and a processor module comprisingprogramming that estimates at least one analyte value for a time periodbased on the data stream, wherein the analyte estimation comprisesperforming an algorithm to estimate an analyte value and applying aphysiological boundary to the estimated analyte value.

In a fourteenth embodiment, a system for displaying analyte data from acontinuous analyte sensor is provided, the system comprising an inputmodule operatively connected to the continuous analyte sensor thatreceives a data stream comprising a plurality of time spaced sensor datapoints from the analyte sensor; a processor module comprisingprogramming that calibrates the data stream using a conversion functionto determine a calibrated analyte value and analyze a variation of thecalibrated analyte value based on statistical, clinical, orphysiological parameters; and an output module associated with theprocessor module and comprising programming to output data based on thecalibrated analyte value and the variation of calibrated analyte value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates the configuration of themedical device in one embodiment, including a continuous analyte sensor,a receiver, and an external device.

FIG. 2 is a flow chart that illustrates the process of measurement andcalibration of the continuous analyte sensor in one embodiment.

FIG. 3 is a flow chart that illustrates the process of estimation ofanalyte values based on measured analyte values in one embodiment.

FIG. 4 is a graph that illustrates the case where estimation istriggered by an event wherein a patient's blood glucose concentrationpasses above a predetermined threshold.

FIG. 5 is a graph that illustrates a raw data stream and correspondingreference analyte values.

FIG. 6 is a flow chart that illustrates the process of compensating fora time lag associated with a continuous analyte sensor to providereal-time estimated analyte data output in one embodiment.

FIG. 7 is a graph that illustrates the data of FIG. 5, includingreference analyte data and corresponding calibrated sensor analyte andestimated sensor analyte data, showing compensation for time lag usingestimation.

FIG. 8 is a flow chart that illustrates the process of matching datapairs from a continuous analyte sensor and a reference analyte sensor inone embodiment.

FIG. 9 is a flow chart that illustrates the process of dynamic andintelligent estimation algorithm selection in one embodiment.

FIG. 10 is a graph that illustrates one case of dynamic and intelligentestimation applied to a data stream, showing first order estimation,second order estimation, and the measured values for a time period,wherein the second order estimation shows a closer correlation to themeasured data than the first order estimation.

FIG. 11 is a flow chart that illustrates the process of estimatinganalyte values within physiological boundaries in one embodiment.

FIG. 12 is a graph that illustrates one case wherein dynamic andintelligent estimation is applied to a data stream, wherein theestimation performs regression and further applies physiologicalconstraints to the estimated analyte data.

FIG. 13 is a flow chart that illustrates the process of dynamic andintelligent estimation and evaluation of analyte values in oneembodiment.

FIG. 14 is a graph that illustrates a case wherein the selectedestimative algorithm is evaluated in one embodiment, wherein acorrelation is measured to determine a deviation of the measured analytedata with the selected estimative algorithm, if any.

FIG. 15 is a flow chart that illustrates the process of evaluating avariation of estimated future analyte value possibilities in oneembodiment.

FIG. 16 is a graph that illustrates a case wherein a variation ofestimated analyte values is based on physiological parameters.

FIG. 17 is a graph that illustrates a case wherein a variation ofestimated analyte values is based on statistical parameters.

FIG. 18 is a flow chart that illustrates the process of estimating,measuring, and comparing analyte values in one embodiment.

FIG. 19 is a graph that illustrates a case wherein a comparison ofestimated analyte values to time corresponding measured analyte valuesis used to determine correlation of estimated to measured analyte data.

FIG. 20 is an illustration of the receiver in one embodiment showing ananalyte trend graph, including measured analyte values, estimatedanalyte values, and a zone of clinical risk.

FIG. 21 is an illustration of the receiver in one embodiment showing agradient bar, including measured analyte values, estimated analytevalues, and a zone of clinical risk.

FIG. 22 is an illustration of the receiver in one embodiment showing ananalyte trend graph, including measured analyte values and one or moreclinically acceptable target analyte values.

FIG. 23 is an illustration of the receiver of FIG. 22, further includingestimated analyte values on the same screen.

FIG. 24 is an illustration of the receiver of FIG. 23, further includinga variation of estimated analyte values and therapy recommendations onthe same screen to help the user obtain the displayed target analytevalues.

FIG. 25 is an illustration of the receiver in one embodiment, showingmeasured analyte values and a dynamic visual representation of a rangeof estimated analyte values based on a variation analysis.

FIG. 26 is an illustration of the receiver in another embodiment,showing measured analyte values and a visual representation of range ofestimated analyte values based on a variation analysis.

FIG. 27 is an illustration of the receiver in another embodiment,showing a numerical representation of the most recent measured analytevalue, a directional arrow indicating rate of change, and a secondarynumerical value representing a variation of the measured analyte value.

FIG. 28 depicts a conventional display of glucose data (uniform y-axis),9-hour trend graph.

FIG. 29 depicts a utility-driven display of glucose data (non-uniformy-axis), 9-hour trend graph.

FIG. 30 depicts a conventional display of glucose data, 7-day glucosechart.

FIG. 31 depicts a utility-driven display of glucose data, 7-day controlchart, median (interquartile range) of daily glucose.

FIG. 32 is an illustration of a receiver in one embodiment thatinterfaces with a computer.

FIG. 33 is an illustration of a receiver in one embodiment thatinterfaces with a modem.

FIG. 34 is an illustration of a receiver in one embodiment thatinterfaces with an insulin pen.

FIG. 35 is an illustration of a receiver in one embodiment thatinterfaces with an insulin pump.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

The following description and examples illustrate some exemplaryembodiments of the disclosed invention in detail. Those of skill in theart will recognize that there are numerous variations and modificationsof this invention that are encompassed by its scope. Accordingly, thedescription of a certain exemplary embodiment should not be deemed tolimit the scope of the present invention.

Definitions

In order to facilitate an understanding of the disclosed invention, anumber of terms are defined below.

The term “analyte,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, to refer to a substanceor chemical constituent in a biological fluid (for example, blood,interstitial fluid, cerebral spinal fluid, lymph fluid or urine) thatcan be analyzed. Analytes can include naturally occurring substances,artificial substances, metabolites, and/or reaction products. In someembodiments, the analyte for measurement by the sensor heads, devices,and methods is analyte. However, other analytes are contemplated aswell, including but not limited to acarboxyprothrombin; acylcarnitine;adenine phosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; camitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A,hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F,D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, IITLV-1,Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax,sexual differentiation, 21-deoxycortisol); desbutylhalofantrine;dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocytearginase; erythrocyte protoporphyrin; esterase D; fattyacids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszkyrs disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain embodiments. The analytecan be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte can be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbituates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHTAA).

The term “continuous analyte sensor,” as used herein, is a broad termand is used in its ordinary sense, including, but not limited to, adevice that continuously or continually measures a concentration of ananalyte, for example, at time intervals ranging from fractions of asecond up to, for example, 1, 2, or 5 minutes, or longer. In oneexemplary embodiment, the continuous analyte sensor is a glucose sensorsuch as described in U.S. Pat. No. 6,001,067, which is incorporatedherein by reference in its entirety.

The term “continuous analyte sensing,” as used herein, is a broad termand is used in its ordinary sense, including, but not limited to, theperiod in which monitoring of an analyte is continuously or continuallyperformed, for example, at time intervals ranging from fractions of asecond up to, for example, 1, 2, or 5 minutes, or longer.

The terms “reference analyte monitor,” “reference analyte meter,” and“reference analyte sensor,” as used herein, are broad terms and are usedin their ordinary sense, including, but not limited to, a device thatmeasures a concentration of an analyte and can be used as a referencefor the continuous analyte sensor, for example a self-monitoring bloodglucose meter (SMBG) can be used as a reference for a continuous glucosesensor for comparison, calibration, or the like.

The term “biological sample,” as used herein, is a broad term and isused in its ordinary sense, including, but not limited to, sample of ahost body, for example, blood, interstitial fluid, spinal fluid, saliva,urine, tears, sweat, or the like.

The term “host,” as used herein, is a broad term and is used in itsordinary sense, including, but not limited to, mammals such as humans.

The term “processor,” as used herein, is a broad term and is used in itsordinary sense, including, but not limited to, a computer system, statemachine, or the like that performs arithmetic and logic operations usinglogic circuitry that responds to and processes the basic instructionsthat drive a computer.

The term “ROM,” as used herein, is a broad term and is used in itsordinary sense, including, but not limited to, read-only memory, whichis a type of data storage device manufactured with fixed contents. ROMis broad enough to include EEPROM, for example, which is electricallyerasable programmable read-only memory (ROM).

The term “RAM,” as used herein, is a broad term and is used in itsordinary sense, including, but not limited to, a data storage device forwhich the order of access to different locations does not affect thespeed of access. RAM is broad enough to include SRAM, for example, whichis static random access memory that retains data bits in its memory aslong as power is being supplied.

The term “A/D Converter,” as used herein, is a broad term and is used inits ordinary sense, including, but not limited to, hardware and/orsoftware that converts analog electrical signals into correspondingdigital signals.

The term “RF transceiver,” as used herein, is a broad term and is usedin its ordinary sense, including, but not limited to, a radio frequencytransmitter and/or receiver for transmitting and/or receiving signals.

The terms “raw data stream” and “data stream,” as used herein, are broadterms and are used in their ordinary sense, including, but not limitedto, an analog or digital signal directly related to the analyteconcentration measured by the analyte sensor. In one example, the rawdata stream is digital data in “counts” converted by an A/D converterfrom an analog signal (for example, voltage or amps) representative ofan analyte concentration. The terms broadly encompass a plurality oftime spaced data points from a substantially continuous analyte sensor,which comprises individual measurements taken at time intervals rangingfrom fractions of a second up to, for example, 1, 2, or 5 minutes orlonger.

The terms “calibrated data” and “calibrated data stream,” as usedherein, are broad terms, and are used in their ordinary sense,including, but not limited to, data that has been transformed from itsraw state to another state using a function, for example a conversionfunction, to provide a meaningful value to a user. The terms “smootheddata” and “filtered data,” as used herein, are broad terms and are usedin their ordinary sense, including, but not limited to, data that hasbeen modified to make it smoother and more continuous and/or to removeor diminish outlying points, for example, by performing a moving averageof the raw data stream.

The term “counts,” as used herein, is a broad term and is used in itsordinary sense, including, but not limited to, a unit of measurement ofa digital signal. In one example, a raw data stream measured in countsis directly related to a voltage (for example, converted by an AIDconverter), which is directly related to current from a workingelectrode.

The term “electronic circuitry,” as used herein, is a broad term and isused in its ordinary sense, including, but not limited to, thecomponents (for example, hardware and/or software) of a deviceconfigured to process data. In the case of an analyte sensor, the dataincludes biological information obtained by a sensor regarding theconcentration of the analyte in a biological fluid. U.S. Pat. Nos.4,757,022, 5,497,772 and 4,787,398, which are hereby incorporated byreference in their entirety, describe suitable electronic circuits thatcan be utilized with devices of certain embodiments.

The term “potentiostat,” as used herein, is a broad term and is used inits ordinary sense, including, but not limited to, an electrical systemthat controls the potential between the working and reference electrodesof a two-electrode cell or three-electrode cell at a preset value. Thepotentiostat forces whatever current is necessary to flow between theworking and counter electrodes to keep the desired potential, as long asthe needed cell voltage and current do not exceed the compliance limitsof the potentiostat.

The term “electrical potential,” as used herein, is a broad term and isused in its ordinary sense, including, but not limited to, theelectrical potential difference between two points in a circuit, whichis the cause of the flow of a current.

The terms “operably connected” and “operably linked,” as used herein,are broad terms and are used in their ordinary sense, including, but notlimited to, one or more components being linked to another component(s)in a manner that allows transmission of signals between the components.For example, one or more electrodes can be used to detect the amount ofglucose in a sample and convert that information into a signal; thesignal can then be transmitted to an electronic circuit. In this case,the electrode is “operably linked” to the electronic circuit. Theseterms are broad enough to include wired and wireless connectivity.

The term “algorithm,” as used herein, is a broad term and is used in itsordinary sense, including, but not limited to, the computationalprocesses (for example, programs) involved in transforming informationfrom one state to another, for example using computer processing.

The term “estimation algorithm,” as used herein, is a broad term and isused in its ordinary sense, including, but not limited to, theprocessing involved in estimating analyte values from measured analytevalues for a time period during which no data exists (e.g., for a futuretime period or during data gaps). This term is broad enough to includeone or a plurality of algorithms and/or computations. This term is alsobroad enough to include algorithms or computations based onmathematical, statistical, clinical, and/or physiological information.

The term “regression,” as used herein, is a broad term and is used inits ordinary sense, including, but not limited to, finding a line inwhich a set of data has a minimal measurement (for example, deviation)from that line. Regression can be linear, non-linear, first order,second order, and so forth. One example of regression is least squaresregression.

The terms “recursive filter” and “auto-regressive algorithm,” as usedherein, are broad terms and are used in their ordinary sense, including,but not limited to, an equation in which includes previous averages arepart of the next filtered output. More particularly, the generation of aseries of observations whereby the value of each observation is partlydependent on the values of those that have immediately preceded it. Oneexample is a regression structure in which lagged response values assumethe role of the independent variables.

The terms “velocity” and “rate of change,” as used herein, are broadterms and are used in their ordinary sense, including, but not limitedto, time rate of change; the amount of change divided by the timerequired for the change. In one embodiment, these terms refer to therate of increase or decrease in an analyte for a certain time period.

The term “acceleration” as used herein, is a broad term and is used inits ordinary sense, including, but not limited to, the rate of change ofvelocity with respect to time. This term is broad enough to includedeceleration.

The term “variation,” as used herein, is a broad term and is used in itsordinary sense, including, but not limited to, a divergence or amount ofchange from a point, line, or set of data. In one embodiment, estimatedanalyte values can have a variation including a range of values outsideof the estimated analyte values that represent a range of possibilitiesbased on known physiological patterns, for example.

The term “deviation,” as used herein, is a broad term and is used in itsordinary sense, including, but not limited to, a statistical measurerepresenting the difference between different data sets. The term isbroad enough to encompass the deviation represented as a correlation ofdata.

The terms “statistical parameters” and “statistical,” as used herein,are broad terms and are used in their ordinary sense, including, but notlimited to, information computed from the values of a sampling of data.For example, noise or variability in data can be statistically measured.

The term “statistical variation,” as used herein, is a broad term and isused in its ordinary sense, including, but not limited to, divergence orchange from a point, line, or set of data based on statisticalinformation. The term “statistical information” is broad enough toinclude patterns or data analysis resulting from experiments, publishedor unpublished, for example.

The term “clinical risk,” as used herein, is a broad term and is used inits ordinary sense, including, but not limited to, an identified dangeror potential risk to the health of a patient based on a measured orestimated analyte concentration, its rate of change, and/or itsacceleration. In one exemplary embodiment, clinical risk is determinedby a measured glucose concentration above or below a threshold (forexample, 80-200 mg/dL) and/or its rate of change.

The term “clinically acceptable,” as used herein, is a broad term and isused in its ordinary sense, including, but not limited to, an analyteconcentration, rate of change, and/or acceleration associated with thatmeasured analyte that is considered to be safe for a patient. In oneexemplary embodiment, clinical acceptability is determined by a measuredglucose concentration within a threshold (for example, 80-200 mg/dL)and/or its rate of change.

The terms “physiological parameters” and “physiological boundaries,” asused herein, are broad terms and are used in their ordinary sense,including, but not limited to, the parameters obtained from continuousstudies of physiological data in humans and/or animals. For example, amaximal sustained rate of change of glucose in humans of about 4 to 5mg/dL/min and a maximum acceleration of the rate of change of about 0.1to 0.2 mg/dL/min² are deemed physiologically feasible limits; valuesoutside of these limits would be considered non-physiological. Asanother example, the rate of change of glucose is lowest at the maximaand minima of the daily glucose range, which are the areas of greatestrisk in patient treatment, thus a physiologically feasible rate ofchange can be set at the maxima and minima based on continuous studiesof glucose data. As a further example, it has been observed that thebest solution for the shape of the curve at any point along glucosesignal data stream over a certain time period (for example, about 20 to30 minutes) is a straight line, which can be used to set physiologicallimits. These terms are broad enough to include physiological parametersfor any analyte.

The terms “individual physiological patterns” and “individual historicalpatterns,” as used herein, are broad terms and are used in theirordinary sense, including, but not limited to, patterns obtained bymonitoring a physiological characteristic, such as glucoseconcentration, in a mammal over a time period. For example, continual orcontinuous monitoring of glucose concentration in humans can recognize a“normal” pattern of turnaround at the human's lowest glucose levels.

The term “physiological variation,” as used herein, is a broad term andis used in its ordinary sense, including, but not limited to, divergenceor change from a point, line, or set of data based on knownphysiological parameters and/or patterns.

The terms “data association” and “data association function,” as usedherein, are broad terms and are used in their ordinary sense, including,but not limited to, a statistical analysis of data and particularly itscorrelation to, or deviation from, a particular line. A data associationfunction is used to show data association. For example, a measuredglucose data stream as described herein can be analyzed mathematicallyto determine its correlation to, or deviation from, an estimated datastream for a corresponding time period; this correlation or deviation isthe data association. Examples of data association functions include,but are not limited to, linear regression, non-linearmapping/regression, rank (for example, non-parametric) correlation,least mean square fit, mean absolute deviation (MAD), and/or meanabsolute relative difference (MARD).

The terms “clinical error grid,” clinical error analysis” and “errorgrid analysis,” as used herein, are broad terms and are used in theirordinary sense, including, but not limited to, an analysis method thatassigns a specific level of clinical risk to an error between two timecorresponding analyte measurements. Examples include Clarke Error Grid,Consensus Grid, mean absolute relative difference, rate grid, or otherclinical cost functions.

The term “Clarke Error Grid,” as used herein, is a broad term and isused in its ordinary sense, including, but not limited to, an error gridanalysis, which evaluates the clinical significance of the differencebetween a reference glucose value and a sensor generated glucose value,taking into account 1) the value of the reference glucose measurement,2) the value of the sensor glucose measurement, 3) the relativedifference between the two values, and 4) the clinical significance ofthis difference. See Clarke et al., “Evaluating Clinical Accuracy ofSystems for Self-Monitoring of Blood Glucose,” Diabetes Care, Volume 10,Number 5, September-October 1987, which is incorporated by referenceherein in its entirety.

The term “rate grid”, as used herein, is a broad term and is used in itsordinary sense, including, without limitation, to refer to an error gridanalysis, which evaluates the clinical significance of the differencebetween a reference glucose value and a continuous sensor generatedglucose value, taking into account both single-point and rate-of-changevalues. One example of a rate grid is described in Kovatchev, B. P.;Gonder-Frederick, L. A.; Cox, D. J.; Clarke, W. L. Evaluating theaccuracy of continuous glucose-monitoring sensors: continuousglucose-error grid analysis illustrated by TheraSense FreestyleNavigator data. Diabetes Care 2004, 27, 1922-1928.

The term “curvature formula,” as used herein, is a broad term and isused in its ordinary sense, including, but not limited to, amathematical formula that can be used to define a curvature of a line.Some examples of curvature formulas include Euler and Rodrigues'curvature formulas.

The term “time period,” as used herein, is a broad term and is used inits ordinary sense, including, but not limited to, an amount of timeincluding a single point in time and a path (for example, range of time)that extends from a first point in time to a second point in time.

The term “measured analyte values,” as used herein, is a broad term andis used in its ordinary sense, including, but not limited to, an analytevalue or set of analyte values for a time period for which analyte datahas been measured by an analyte sensor. The term is broad enough toinclude data from the analyte sensor before or after data processing inthe sensor and/or receiver (for example, data smoothing, calibration, orthe like).

The term “estimated analyte values,” as used herein, is a broad term andis used in its ordinary sense, including, but not limited to, an analytevalue or set of analyte values, which have been algorithmicallyextrapolated from measured analyte values. Typically, estimated analytevalues are estimated for a time period during which no data exists.However, estimated analyte values can also be estimated during a timeperiod for which measured data exists, but is to be replaced byalgorithmically extrapolated data due to a time lag in the measureddata, for example.

The term “alarm,” as used herein, is a broad term and is used in itsordinary sense, including, but not limited to, audible, visual, ortactile signals that are triggered in response to detection of clinicalrisk to a patient. In one embodiment, hyperglycemic and hypoglycemicalarms are triggered when present or future clinical danger is assessedbased on continuous analyte data.

The terms “target analyte values” and “analyte value goal,” as usedherein, are broad terms and are used in their ordinary sense, including,but not limited to, an analyte value or set of analyte values that areclinically acceptable. In one example, a target analyte value isvisually or audibly presented to a patient in order to aid in guidingthe patient in understanding how they should avoid a clinically riskyanalyte concentration.

The terms “therapy” and “therapy recommendations,” as used herein, arebroad terms and are used in their ordinary sense, including, but notlimited to, the treatment of disease or disorder by any method. In oneexemplary embodiment, a patient is prompted with therapy recommendationssuch as “inject insulin” or “consume carbohydrates” in order to avoid aclinically risky glucose concentration.

The terms “customize” and “customization,” as used herein, are broadterms and are used in their ordinary sense, including, but not limitedto, to make changes or specifications to a program so that it meets anindividual's needs.

The term “computer,” as used herein, is broad term and is used in itsordinary sense, including, but not limited to, machine that can beprogrammed to manipulate data.

The term “modem,” as used herein, is a broad term and is used in itsordinary sense, including, but not limited to, an electronic device forconverting between serial data from a computer and an audio signalsuitable for transmission over a telecommunications connection toanother modem.

The term “insulin pen,” as used herein, is a broad term and is used inits ordinary sense, including, but not limited to, an insulin injectiondevice generally the size of a pen that includes a needle and holds avial of insulin. It can be used instead of syringes for giving insulininjections.

The term “insulin pump,” as used herein, is a broad term and is used inits ordinary sense, including, but not limited to, a device thatdelivers a continuous supply of insulin into the body. The insulin flowsfrom the pump through a plastic tube (called a catheter) that isconnected to a needle inserted into the skin and taped in place, forexample.

Overview

Certain embodiments provide a continuous analyte sensor that measures aconcentration of analyte within a host and provides a data streamrepresentative of the concentration of the analyte in the host, and areceiver that processes the data stream received from the analyte sensorfor output as a meaningful value to a user or device. In someembodiments, the analyte sensor is integral with the receiver, while inother embodiments, the analyte sensor is operatively linked to thereceiver, for example, via a wired link or a wireless link.

Data processing associated with various embodiments calculates estimatedanalyte values from measured analyte values that can be useful to 1)compensate for a time lag associated with the analyte concentrationmeasured sensor as compared to a reference source, for example, 2)estimate approaching clinical risk and warn a patient or doctor in aneffort to avoid the clinical risk, 3) ensure accurate calibration ofsensor data with reference data by dynamically and intelligentlymatching reference data with corresponding sensor data, for example, 4)replace data during periods of high signal noise or inaccurate data,and/or 5) provide future estimated analyte values that encourage moretimely proactive behavior by a patient. The systems and methodscalculate estimated analyte values based on algorithms that dynamicallyand intelligently determine which estimative algorithm best fits thepresent data stream, for example, using first or second orderregression, considering physiological boundaries, evaluating theestimative algorithm for data association, determining possiblevariations around the estimated analyte values due to statistical,clinical, or physiological considerations, and/or comparing theestimated analyte values with time corresponding measured analytevalues.

Some embodiments further generate data output, which can be in the formof real-time output to a user on screen or other user interface, forexample, on the receiver. Data output can include real-time measuredanalyte values, estimated analyte values, possible variations ofestimated analyte values, targets or goals for analyte values, or thelike. Additionally or alternatively, data output can be sent to a deviceexternal from the receiver, for example, a computer, modem, or medicaldevice. In some embodiments, input from the user or from another device,such as insulin injections (time and amount), meal times, exercise,personalized therapy recommendations, or the like, can be input into thereceiver and processed to provide more customized data analysis and/ordata output.

Accordingly, the systems and methods calculate estimated analyte valuesin a timely, accurate, and reliable manner based on measured analytevalues, which can be helpful for proactively caring for a patient'scondition. Estimated analyte values can provide information useful inwarning a patient of upcoming clinical risk. Additionally, targetsand/or goals set for a patient's analyte values, based on presentanalyte conditions, and can be useful in proactively avoiding clinicalrisk. Furthermore, therapy recommendations can be provided that areuseful in guiding a patient away from clinical risk.

Continuous Analyte Sensor

The systems and methods of the preferred embodiments provide an analytesensor that measures a concentration of analyte of interest or asubstance indicative of the concentration or presence of the analyte.The analyte sensor uses any known method, including invasive, minimallyinvasive, and non-invasive sensing techniques, to provide an outputsignal indicative of the concentration of the analyte of interest. Insome embodiments, the analyte sensor is a continuous device, for examplea subcutaneous, transdermal, or intravascular device. In someembodiments, the device can take a plurality of intermittentmeasurements. The analyte sensor can use any method ofanalyte-measurement, including enzymatic, chemical, physical,electrochemical, spectrophotometric, polarimetric, calorimetric,radiometric, or the like. Generally, the analyte sensor can be anysensor capable of determining the level of any analyte in the body, forexample glucose, oxygen, lactase, hormones, cholesterol, medicaments,viruses, or the like. It should be understood that the devices andmethods described herein can be applied to any device capable ofcontinually or continuously detecting a concentration of analyte andproviding an output signal that represents the concentration of thatanalyte.

In one preferred embodiment, the analyte sensor is an implantableglucose sensor, such as described with reference to U.S. Pat. No.6,001,067 and co-pending U.S. patent application Ser. No. 10/633,367entitled, “SYSTEM AND METHODS FOR PROCESSING ANALYTE SENSOR DATA,” filedAug. 1, 2003, which are incorporated herein by reference in theirentirety. In another preferred embodiment, the analyte sensor is atranscutaneous glucose sensor, such as described with reference to U.S.Provisional Patent Application 60/587,787 and 60/614,683. In onealternative embodiment, the continuous glucose sensor comprises atranscutaneous sensor such as described in U.S. Pat. No. 6,565,509 toSay et al., for example. In another alternative embodiment, thecontinuous glucose sensor comprises a subcutaneous sensor such asdescribed with reference to U.S. Pat. No. 6,579,690 to Bonnecaze et al.or U.S. Pat. No. 6,484,046 to Say et al., for example. In anotheralternative embodiment, the continuous glucose sensor comprises arefillable subcutaneous sensor such as described with reference to U.S.Pat. No. 6,512,939 to Colvin et al., for example. In another alternativeembodiment, the continuous glucose sensor comprises an intravascularsensor such as described with reference to U.S. Pat. No. 6,477,395 toSchulman et al., for example. In another alternative embodiment, thecontinuous glucose sensor comprises an intravascular sensor such asdescribed with reference to U.S. Pat. No. 6,424,847 to Mastrototaro etal. All of the above patents are incorporated by reference herein intheir entirety.

FIG. 1 is a block diagram that illustrates the configuration of themedical device in one embodiment, including a continuous analyte sensor,a receiver, and an external device. In general, the continuous analytesensor 10 is any sensor configuration that provides an output signalindicative of a concentration of an analyte. The output signal is sentto a receiver 12 and received by an input module 14, which is describedin more detail below. The output signal is typically a raw data streamthat is used to provide a useful value of the measured analyteconcentration to a patient or doctor, for example. In some embodiments,the raw data stream can be continuously or periodically algorithmicallysmoothed or otherwise modified to diminish outlying points that do notaccurately represent the analyte concentration, for example due tosignal noise or other signal artifacts, such as described in co-pendingU.S. patent application Ser. No. 10/632,537 entitled, “SYSTEMS ANDMETHODS FOR REPLACING SIGNAL ARTIFACTS IN A GLUCOSE SENSOR DATA STREAM,”filed Aug. 22, 2003, which is incorporated herein by reference in itsentirety.

Receiver

Referring again to FIG. 1, the receiver 12, which is operatively linkedto the sensor 10, receives a data stream from the sensor 10 via theinput module 14. In one embodiment, the input module includes a quartzcrystal operably connected to an RF transceiver (not shown) thattogether function to receive and synchronize data streams from thesensor 10. However, the input module 14 can be configured in any mannerthat is capable of receiving data from the sensor. Once received, theinput module 14 sends the data stream to a processor 16 that processesthe data stream, such as described in more detail below.

The processor 16 is the central control unit that performs theprocessing, such as storing data, analyzing data streams, calibratinganalyte sensor data, estimating analyte values, comparing estimatedanalyte values with time corresponding measured analyte values,analyzing a variation of estimated analyte values, downloading data, andcontrolling the user interface by providing analyte values, prompts,messages, warnings, alarms, or the like. The processor includes hardwareand software that performs the processing described herein, for exampleread-only memory (ROM) provides permanent or semi-permanent storage ofdata, storing data such as sensor ID, receiver ID, and programming toprocess data streams (for example, programming for performing estimationand other algorithms described elsewhere herein) and random accessmemory (RAM) stores the system's cache memory and is helpful in dataprocessing.

An output module 18, which is integral with and/or operatively connectedwith the processor 16, includes programming for generating output basedon the data stream received from the sensor 10 and its processingincurred in the processor 16. In some embodiments, output is generatedvia a user interface 20.

The user interface 20 comprises a keyboard 22, speaker 24, vibrator 26,backlight 28, liquid crystal display (LCD) screen 30, and one or morebuttons 32. The components that comprise the user interface 20 includecontrols to allow interaction of the user with the receiver. Thekeyboard 22 can allow, for example, input of user information abouthimself/herself, such as mealtime, exercise, insulin administration,customized therapy recommendations, and reference analyte values. Thespeaker 24 can produce, for example, audible signals or alerts forconditions such as present and/or estimated hyper- and hypoglycemicconditions in a person with diabetes. The vibrator 26 can provide, forexample, tactile signals or alerts for reasons such as described withreference to the speaker, above. The backlight 28 can be provided, forexample, to aid the user in reading the LCD 30 in low light conditions.The LCD 30 can be provided, for example, to provide the user with visualdata output, such as described in more detail below with reference toFIGS. 20 to 26, however other screen formats are possible. In someembodiments, the LCD is a touch-activated screen. The buttons 32 canprovide for toggle, menu selection, option selection, mode selection,and reset, for example. In some alternative embodiments, a microphonecan be provided to allow for voice-activated control.

In some embodiments, estimated analyte values, such as described, forexample with reference to FIGS. 3 to 14, can be displayed on the LCD 30.In some embodiments, a variation of estimated analyte values, such asdescribed, for example with reference to FIGS. 15 to 17, can bedisplayed on the LCD 30. In some embodiments, target analyte values,such as described, for example with reference to FIGS. 22 to 24, can bedisplayed on the LCD 30. In some embodiments, therapy recommendations,such as described in the preferred embodiments, for example withreference to FIG. 24, can be displayed on the screen 30.

In some embodiments, prompts or messages can be displayed on the userinterface to convey information to the user, such as reference outliervalues, requests for reference analyte values, therapy recommendations,deviation of the measured analyte values from the estimated analytevalues, or the like. Additionally, prompts can be displayed to guide theuser through calibration or trouble-shooting of the calibration.

Additionally, data output from the output module 18 can provide wired orwireless, one- or two-way communication between the receiver 12 and anexternal device 34. The external device 34 can be any device thatwherein interfaces or communicates with the receiver 12. In someembodiments, the external device 34 is a computer, and the receiver 12is able to download historical data for retrospective analysis by thephysician, for example. In some embodiments, the external device 34 is amodem, and the receiver 12 is able to send alerts, warnings, emergencymessages, or the like, via telecommunication lines to another party,such as a doctor or family member. In some embodiments, the externaldevice 34 is an insulin pen, and the receiver 12 is able to communicatetherapy recommendations, such as insulin amount and time to the insulinpen. In some embodiments, the external device 34 is an insulin pump, andthe receiver 12 is able to communicate therapy recommendations, such asinsulin amount and time to the insulin pump. The external device 34 caninclude other technology or medical devices, for example pacemakers,implanted analyte sensor patches, other infusion devices, telemetrydevices, or the like.

The user interface 20 including keyboard 22, buttons 32, a microphone(not shown), and the external device 34 can be configured to allow inputof data. Data input can be helpful in obtaining information about thepatient (for example, meal time, exercise, or the like), receivinginstructions from a physician (for example, customized therapyrecommendations, targets, or the like), and downloading softwareupdates, for example. Keyboard, buttons, touch-screen, and microphoneare all examples of mechanisms by which a user can input data directlyinto the receiver. A server, personal computer, personal digitalassistant, insulin pump, and insulin pen are examples of externaldevices that can provide useful information to the receiver. Otherdevices internal or external to the sensor that measure other aspects ofa patient's body (for example, temperature sensor, accelerometer, heartrate monitor, oxygen monitor, or the like) can be used to provide inputhelpful in data processing. In one embodiment, the user interface canprompt the patient to select an activity most closely related to theirpresent activity, which can be helpful in linking to an individual'sphysiological patterns, or other data processing. In another embodiment,a temperature sensor and/or heart rate monitor can provide informationhelpful in linking activity, metabolism, and glucose excursions of anindividual. While a few examples of data input have been provided here,a variety of information can be input, which can be helpful in dataprocessing as will be understood by one skilled in the art.

Calibration

Reference is now made to FIG. 2, which is a flow chart that illustratesthe process 38 of calibration and data output of measured analyte valuesin one embodiment. Calibration of the analyte sensor 10 generallyincludes data processing that converts the data stream received from thecontinuous analyte sensor into measured analyte values that aremeaningful to a user. In one embodiment, the analyte sensor is acontinuous glucose sensor and one or more reference glucose values areused to calibrate the data stream from the sensor 10. The calibrationcan be performed on a real-time basis and/or retrospectivelyrecalibrated. However in alternative embodiments, other calibrationtechniques can be utilized, for example using another constant analyte(for example, folic acid, ascorbate, urate, or the like) as a baseline,factory calibration, periodic clinical calibration, oxygen calibration(for example, using a plurality of sensor heads), or the like can beused.

At a block 40, the calibration process 38 receives continuous sensordata (for example, a data stream), including one or more time-spacedsensor data points, hereinafter referred to as “data stream,” “sensordata,” or “sensor analyte data.” The calibration process 38 receives thesensor data from the continuous analyte sensor 10, which can be incommunication (for example, wired or wireless) with the receiver 12.Some or all of the sensor data point(s) can be smoothed or replaced byestimated signal values such as described with reference to co-pendingU.S. patent application Ser. No. 10/632,537 entitled, “SYSTEMS ANDMETHODS FOR REPLACING SIGNAL ARTIFACTS IN A GLUCOSE SENSOR DATA STREAM,”filed Aug. 22, 2003, which is incorporated herein by reference in itsentirety. During the initialization of the sensor, for example, prior toinitial calibration, the receiver 12 receives and stores the sensordata, however it may not display any data to the user until initialcalibration and optionally stabilization of the sensor 10 has beendetermined.

At a block 42, the calibration process 38, receives analyte values froma reference analyte monitor, including one or more reference glucosedata points, hereinafter referred as “reference data” or “referenceanalyte data.” In an example wherein the analyte sensor is a continuousglucose sensor, the reference analyte monitor can be a self-monitoringblood glucose (SMBG) meter. However, in alternative embodiments, thereference analyte monitor can be any source capable of providing acorresponding analyte value. Additionally, in some alternativeembodiments, wherein the continuous analyte sensor is self-calibrating,a calibrating reference value can be provided by a source internal tothe continuous sensor, for example oxygen, folic acid, or othersubcutaneous fluid constants.

In some embodiments, the calibration process 38 monitors the continuousanalyte sensor data stream to determine a preferred time for capturingreference analyte concentration values for calibration of the continuoussensor data stream. In an example wherein the analyte sensor is acontinuous glucose sensor, when data (for example, observed from thedata stream) changes too rapidly, the reference glucose value may not besufficiently reliable for calibration due to unstable glucose changes inthe host. In contrast, when sensor glucose data are relatively stable(for example, relatively low rate of change), a reference glucose valuecan be taken for a reliable calibration. In one embodiment, thecalibration process 38 can prompt the user via the user interface to“calibrate now” when the analyte sensor is considered stable.

In some embodiments, the calibration process 38 can prompt the user viathe user interface 20 to obtain a reference analyte value forcalibration at intervals, for example when analyte concentrations are athigh and/or low values. In some additional embodiments, the userinterface 20 can prompt the user to obtain a reference analyte value forcalibration based upon certain events, such as meals, exercise, largeexcursions in analyte levels, faulty or interrupted data readings, orthe like. In some embodiments, the estimative algorithms can provideinformation useful in determining when to request a reference analytevalue. For example, when estimated analyte values indicate approachingclinical risk, the user interface 20 can prompt the user to obtain areference analyte value.

In some embodiments, certain acceptability parameters can be set forreference values. In an example wherein the analyte sensor is a glucosesensor, the receiver may only accept reference glucose data betweenabout 40 and about 400 mg/dL.

In some embodiments, the calibration process 38 performs outlierdetection on the reference data and time corresponding sensor data.Outlier detection compares a reference analyte value with a timecorresponding measured analyte value to ensure a predeterminedstatistically, physiologically, or clinically acceptable correlationbetween the corresponding data exists. In an example wherein the analytesensor is a glucose sensor, the reference glucose data is matched withsubstantially time corresponding calibrated sensor data and the matcheddata are plotted on a Clarke Error Grid to determine whether thereference analyte value is an outlier based on clinical acceptability,such as described in more detail with reference U.S. patent applicationSer. No. 10/633,367 entitled, “SYSTEM AND METHODS FOR PROCESSING ANALYTESENSOR DATA,” filed Aug. 1, 2003, which is incorporated herein byreference in its entirety. In some embodiments, outlier detectioncompares a reference analyte value with a corresponding estimatedanalyte value, such as described in more detail with reference to FIGS.7 and 8, and the matched data is evaluated using statistical, clinical,and/or physiological parameters to determine the acceptability of thematched data pair. In alternative embodiments, outlier detection can bedetermined by other clinical, statistical, and/or physiologicalboundaries.

At a block 44, the calibration process 38 matches reference analyte data(for example, one or more reference glucose data points) withsubstantially time corresponding sensor analyte data (for example, oneor more sensor glucose data points) to provide one or more matched datapairs. In one embodiment, one reference data point is matched to onetime corresponding sensor data point to form a matched data pair. Inanother embodiment, a plurality of reference data points are averaged(for example, equally or non-equally weighted average, mean-value,median, or the like) and matched to one time corresponding sensor datapoint to form a matched data pair. In another embodiment, one referencedata point is matched to a plurality of time corresponding sensor datapoints averaged to form a matched data pair. In yet another embodiment,a plurality of reference data points are averaged and matched to aplurality of time corresponding sensor data points averaged to form amatched data pair.

In one embodiment, a time corresponding sensor data comprises one ormore sensor data points that occur, for example, 15±5 min after thereference glucose data timestamp (for example, the time that thereference glucose data is obtained). In this embodiment, the 15 minutetime delay has been chosen to account for an approximately 10 minutedelay introduced by the filter used in data smoothing and anapproximately 5 minute membrane-related time lag (for example, the timenecessary for the glucose to diffuse through a membrane(s) of a glucosesensor). In alternative embodiments, the time corresponding sensor valuecan be more or less than in the above-described embodiment, for example±60 minutes. Variability in time correspondence of sensor and referencedata can be attributed to, for example, a longer or shorter time delayintroduced during data smoothing, or if the configuration of the glucosesensor 10 incurs a greater or lesser physiological time lag. In someembodiments, estimated sensor data can be used to provide data pointsthat occur about 1 second to about 60 minutes, or more, after areference analyte value is obtained, which data can be used to matchwith reference analyte data, such as described in more detail below withreference to FIGS. 7 and 8.

At a block 46 the calibration process 38 forms an initial calibrationset from a set of one or more matched data pairs, which are used todetermine the relationship between the reference analyte data and thesensor analyte data, such as described in more detail with reference toa block 48, below.

The matched data pairs, which make up the initial calibration set, canbe selected according to predetermined criteria. In some embodiments,the number (n) of data pair(s) selected for the initial calibration setis one. In other embodiments, n data pairs are selected for the initialcalibration set wherein n is a function of the frequency of the receivedreference glucose data points. In one exemplary embodiment, six datapairs make up the initial calibration set. In another embodiment, thecalibration set includes only one data pair.

In some embodiments, the data pairs are selected only within a certainglucose value threshold, for example wherein the reference glucose valueis between about 40 and about 400 mg/dL. In some embodiments, the datapairs that form the initial calibration set are selected according totheir time stamp.

At the block 48, the calibration process 38 calculates a conversionfunction using the calibration set. The conversion functionsubstantially defines the relationship between the reference analytedata and the sensor analyte data. A variety of known methods can be usedwith the preferred embodiments to create the conversion function fromthe calibration set. In one embodiment, wherein a plurality of matcheddata points form the initial calibration set, a linear least squaresregression is performed on the initial calibration set. Co-pending U.S.patent application Ser. No. 10/633,367 entitled, “SYSTEM AND METHODS FORPROCESSING ANALYTE SENSOR DATA,” filed Aug. 1, 2003, which isincorporated herein by reference in its entirety describes methods forcalibration.

In one embodiment, the conversion function can be used to estimateanalyte values for a future time period by forward projection. Inalternative preferred embodiments, such as described with reference tothe flow chart of FIG. 2 and with reference to FIGS. 3 to 19, theprocessor can provide intelligent estimation, including dynamicdetermination of an algorithm, physiological boundaries, evaluation ofthe estimative algorithm, analysis of variations associated with theestimation, and comparison of measured analyte values with timecorresponding estimated analyte values.

At a block 50, the calibration process 38 uses the conversion functionto transform sensor data into substantially measured analyte values,also referred to as calibrated data, as sensor data is continuously (orintermittently) received from the sensor. For example, the offset valueat any given point in time can be subtracted from the raw value (forexample, in counts) and divided by the slope to obtain a measuredglucose value:

${{Glucose}\mspace{14mu} {Concentration}} = \frac{\left( {{rawvalue} - {offset}} \right)}{slope}$

In some alternative embodiments, the sensor and/or reference glucosedata are stored in a database for retrospective analysis. The calibrateddata can be used to compare with the estimated analyte values, such asdescribed in more detail with reference to FIG. 10 in order to determinea deviation of the measure value from the estimated analyte values forthe corresponding time period.

At a block 52, the calibration process 38 generates output via the userinterface 20 and/or the external device 34. In one embodiment, theoutput is representative of measured analyte values, which aredetermined by converting the sensor data into a meaningful analyte valuesuch as described in more detail with reference to block 50, above. Useroutput can be in the form of a numeric estimated analyte value, anindication of directional trend of analyte concentration, and/or agraphical representation of the measured analyte data over a period oftime, for example. Other representations of the measured analyte valuesare also possible, for example audio and tactile. Additionally oralternatively, the output is representative of estimated analyte values,such as described in more detail with reference to FIGS. 20 to 26.

In one embodiment, the measured analyte value is represented by anumeric value. In other exemplary embodiments, the user interfacegraphically represents the measured analyte trend values over apredetermined time period (for example, one, three, and nine hours,respectively). In alternative embodiments, other time periods can berepresented. In alternative embodiments, pictures, animation, charts,graphs, and numeric data can be selectively displayed.

Accordingly, after initial calibration of the sensor, continuous analytevalues can be displayed on the user interface 20 so that the user canregularly and proactively care for his/her diabetic condition within thebounds set by his/her physician. Both the reference analyte data and thesensor analyte data from the continuous analyte sensor can be displayedto the user. In an embodiment wherein the continuous analyte sensorfunctions as an adjunctive device to a reference analyte monitor, theuser interface 20 can display numeric reference analyte data, whileshowing the sensor analyte data only in a graphical representation sothat the user can see the historical and present sensor trendinformation as well as the most recent reference analyte data value. Inan embodiment wherein the continuous analyte sensor functions as anon-adjunctive device to the reference analyte monitor, the userinterface can display the reference analyte data and/or the sensoranalyte data. The user can toggle through menus and screens using thebuttons in order to view alternate data and/or screen formats, forexample.

In alternative embodiments, the output module displays the estimatedanalyte values in a manner such as described in more detail withreference to FIGS. 20 to 26, for example. In some embodiments, themeasured analyte value, an estimated future analyte value, a rate ofchange, and/or a directional trend of the analyte concentration is usedto control the administration of a constituent to the user, including anappropriate amount and time, in order to control an aspect of the user'sbiological system. One such example is a closed loop glucose sensor andinsulin pump, wherein the glucose data (for example, estimated glucosevalue, rate of change, and/or directional trend) from the glucose sensoris used to determine the amount of insulin, and time of administration,that can be given to a person with diabetes to evade hyperglycemic andhypoglycemic conditions. Output to external devices is described in moredetail with reference to FIGS. 27 to 30, for example.

Dynamic and Intelligent Analyte Value Estimation

Estimative algorithms can be applied continuously, or selectively turnedon/off based on conditions. Conventionally, a data stream received froma continuous analyte sensor can provide an analyte value and output thesame to the host, which can be used to warn a patient or doctor ofexisting clinical risk. Conventionally, a data stream received from ananalyte sensor can provide historical trend analyte values, which can beused to educate a patient or doctor of individual historical trends ofthe patient's analyte concentration. However, the data stream cannot,without additional processing, provide future analyte values, which canbe useful in preventing clinically risky analyte values, compensatingfor time lag, and ensuring proper matching of sensor and referenceanalyte, for example such as described below. Timelier reporting ofanalyte values and prevention of clinically risky analyte values, forexample, prevention of hyper- and hypoglycemic conditions in a personwith diabetes, can decrease health complications that can result fromclinically risky situations.

FIG. 3 is a flow chart that illustrates the process 54 of estimatinganalyte values and outputting estimated analyte values in oneembodiment. In contrast to the process of FIG. 2, estimation is used tocalculate analyte data for time during which no data exists (forexample, data gaps or future data) or to replace data when largeinaccuracies are believed to exist within data (for example, signalnoise due to transient ischemia). Estimation of analyte values can beperformed instead of, or in combination with, calibration of measuredanalyte values, such as described with reference to FIG. 2, above.

The estimating analyte values process 54 can be applied continuously, orselectively turned on/off based on conditions. The determination of whento apply estimative algorithms is discussed in more detail below. Insome embodiments, estimation can be applied only during approachingclinical risk to warn a patient or doctor in an effort to avoid theclinical risk, for example when the measured glucose concentration isoutside of a clinically acceptable threshold (for example, 100 to 200mg/dL) and/or the glucose concentration is increasing or decreasing at acertain rate of change (for example, 3 mg/dL/min), such as described inmore detail with reference to FIG. 4, for example. In some embodimentsestimation can be applied continuously, dynamically, or intermittentlyto compensate for a time lag associated with the analyte sensor, whichtime lag can be consistent, dynamic, and/or intermittent, such asdescribed in more detail below with reference to FIGS. 5 to 6, forexample. In some embodiments, estimation can be applied to aid indynamically and intelligently matching reference data with correspondingsensor data to ensure accurate outlier detection and/or calibration ofsensor data with reference data, such as described in more detail withreference to FIGS. 7 and 8, for example. In some embodiments, estimationcan be applied continuously (or intermittently) in order to provideanalyte data that encourages more timely proactive behavior inpreempting clinical risk.

At a block 56, the estimate analyte values process 54 obtains sensordata, which can be raw, smoothed, and/or otherwise processed. In someembodiments, estimation can be applied to a raw data stream receivedfrom an analyte sensor, such as described at the block 40. In someembodiments, estimation can be applied to calibrated data, such asdescribed at the block 50.

At a block 58, the estimate analyte values process 54 dynamically andintelligently estimates analyte values based on measured analyte valuesusing estimative algorithms. In some embodiments, dynamic andintelligent estimation includes selecting an algorithm from a pluralityof algorithms to determine an estimative algorithm (for example, firstor second order regression) that best fits the present measured analytevalues, such as described in more detail with reference to FIGS. 9 and10, for example. In some embodiments, dynamic and intelligent estimationfurther includes constraining and/or expanding estimated analyte valuesusing physiological parameters, such as described in more detail withreference to FIGS. 11 and 12, for example. In some embodiments, dynamicand intelligent estimation further includes evaluating the selectedestimative algorithms, for example using a data association function,such as described in more detail with reference to FIGS. 9, 10, 13, and14. In some embodiments, dynamic and intelligent estimation includesanalyzing a possible variation associated with the estimated analytevalues, for example using statistical, clinical, or physiologicalvariations, such as described in more detail with reference to FIGS. 15to 17. In some embodiments, dynamic and intelligent estimation includescomparing previously estimated analyte values with measured analytevalues for a corresponding time period, determining the deviation, suchas described with reference to FIGS. 18 and 19, for example. In someembodiments, the resulting deviation from the comparison can be used todetermine a variation for future estimated analyte values. In someembodiments, the resulting deviation from the comparison can be used todetermine a confidence level in the estimative algorithms. In someembodiments, the resulting deviation from the comparison can be used toshow evidence of the benefits of displaying estimated analyte values onpatient behavior, namely how well the patient responds to the estimatedanalyte values and alters his/her behavior in order to better controlanalyte levels.

At a block 60, the output module 18 provides output to the userinterface 20 and/or the external device 34. In some embodiments, outputof estimated analyte values is combined with output of measured analytevalues, such as described at the block 52, for example combined on anLCD screen, or by toggling between screens. In some embodiments, atarget analyte value or range of analyte values is output to the userinterface alone, or in combination with the estimated analyte values, inorder to provide a goal towards which the user can aim, such asdescribed with reference to FIGS. 22 to 24, for example. In someembodiments, an approaching clinical risk is output in the form of avisual, audible, or tactile prompt, such as described with reference toFIGS. 20 to 22, for example. In some embodiments, therapyrecommendations are output to aid the user in determining correctiveaction that can be performed in an effort to avoid or minimize clinicalrisk such as described with reference to FIG. 24, for example. In someembodiments, a visual representation of possible variations of theestimated analyte values, which variation can be due to statistical,clinical, or physiological considerations, such as described withreference to FIGS. 24 to 26, for example. In some embodiments, theoutput prompts a user to obtain a reference analyte value (not shown).In some embodiments, output is sent to an external device such asdescribed with reference to FIGS. 27 to 30, for example.

FIG. 4 is a graph that illustrates one embodiment, wherein estimation istriggered by an event such as a patient's blood glucose concentrationrising above a predetermined threshold (for example, 180 mg/dL). Thex-axis represents time in minutes; the y-axis represents glucoseconcentration in mg/dL. The graph shows an analyte trend graph,particularly, the graph shows measured glucose data 62 for about 90minutes up to time (t)=0. In this embodiment, the measured glucose data62 has been smoothed and calibrated, however smoothing and/orcalibrating may not be required in some embodiments. At t=0, estimationof the preferred embodiments is invoked and 15-minute estimated glucosedata 64 indicates that the glucose concentration will likely rise above220 mg/dL. The estimated glucose data 64 can be useful in providingalarms (e.g., hyper- and hypoglycemic alerts) and/or displaying on theuser interface of the receiver, for example. Alarms may not requireestimative algorithms in some embodiments, for example when zero, first,and/or second order calculations can be made to dynamically assess thestatic value, rate of change, and/or rate of acceleration of the analytedata in some embodiment.

In some embodiments, estimative algorithms are selectively applied whenthe reference and/or sensor analyte data indicates that the analyteconcentration is approaching clinical risk. The concentration of theanalyte values, the rate of change of the analyte values, and/or theacceleration of the analyte values can provide information indicative ofapproaching clinical risk. In an example wherein the analyte sensor is aglucose sensor, thresholds (for example, 100 to 200 mg/dL) can be setthat selectively turn on estimative algorithms that then dynamically andintelligently estimate upcoming glucose values, and optionally possiblevariations of those estimated glucose values, to appropriately forewarnof an upcoming patient clinical risk (for example, hypo- orhyperglycemia). Additionally, the rate of change and/or acceleration canbe considered to more intelligently turn on and calculate necessaryestimation and for alarms (e.g., hyper- and hypoglycemic alarms). Forexample, if a person with diabetes has a glucose concentration of 100mg/dL, but is trending upwardly, has slow or no rate of change, or isdecelerating downwardly, estimation and/or alarms may not be necessary.

FIG. 5 is a graph that illustrates a raw data stream and thecorresponding reference analyte values. The x-axis represents time inminutes, the first y-axis represents sensor glucose data measured incounts, and the second y-axis represents reference glucose data inmg/dL. A raw data stream 66 was obtained for a host from a continuousglucose sensor over a 4-hour time period. In this example, the raw datastream 66 has not been smoothed, calibrated, or otherwise processed andis represented in counts. Reference glucose values 68 were obtained fromthe host using a reference glucose monitor during the same 4-hour timeperiod. The raw data stream 66 and reference glucose values 68 wereplotted on the graph of FIG. 5 accordingly during the 4-hour timeperiod. While not wishing to be bound by theory, the visible differencebetween the reference and sensor glucose data is believed to be causedat least in part by a time lag, such as described in more detail below.

A data stream received from an analyte sensor can include a time lagwithin the measured analyte concentration, for example, as compared tocorresponding F reference analyte values. In some embodiments, a timelag can be associated with a difference in measurement samples (forexample, an interstitial fluid sample measured by an implantable analytesensor as compared with a blood sample measured by an external referenceanalyte monitor). In some embodiments, a time lag can be associated withdiffusion of the analyte through a membrane system, for example such ashas been observed in some implantable electrochemically-based glucosesensors. Additionally in some embodiments, a time lag can be associatedwith processing of the data stream, for example, a finite impulseresponse filter (FIR) or infinite impulse response (IIR) filter can beapplied intermittently or continuously to a raw data stream in thesensor (or in the receiver) in order to algorithmically smooth the datastream, which can produce a time lag (for example, as shown in measuredglucose data 68 of FIG. 4B). In some embodiments, wherein the analytesensor is a subcutaneously implantable sensor, there may be a variabletime lag associated with the tissue ingrowth at the biointerface at thetissue-device interface. Additionally, time lags can be variable upon ahost's metabolism. In some embodiments, a time lag of the referenceanalyte data may be associated with an amount of time a user takes totest and report a reference analyte value. Accordingly, the preferredembodiments provide for estimation of analyte values based on measuredanalyte values, which can be used to compensate for a time lag such asdescribed above, allow for output of analyte values that representestimated present analyte values without a time lag.

Accordingly, some embodiments selectively apply estimative algorithmsbased on a measured, estimated, or predetermined time lag associatedwith the continuous analyte sensor. In some embodiments, estimativealgorithms continuously run in order to continuously compensate for atime lag between reference and sensor data, such as described in moredetail below. In some embodiments, estimative algorithms run duringoutlier detection in order to intelligently and dynamically matchcorresponding reference and sensor data for more accurate outlierinclusion or exclusion, such as described in more detail below. In someembodiments, estimative algorithms run during matching of data pairs forconsideration in the calibration set in order to intelligently anddynamically match corresponding reference and sensor glucose data forbetter calibration, such as described in more detail below.

FIG. 6 is a flow chart that illustrates the process 70 of compensatingfor a time lag associated with a continuous analyte sensor to providereal-time estimated analyte data output in one embodiment. For thereasons described above, the system includes programming thatcontinuously or periodically (e.g., when a user activates the LCDscreen) compensates for a time lag in the system to provide a betterreal-time estimate to the user, for example.

At block 72, the time lag compensation process 70 obtains sensor data,which can be raw, smoothed, and/or otherwise processed. In someembodiments, estimation can be applied to a raw data stream receivedfrom an analyte sensor, such as described at the block 40. In someembodiments, estimation can be applied to calibrated data, such asdescribed at the block 50.

At block 74, the time lag compensation process 70 continuously orperiodically estimates analyte values for a present time period tocompensate for a physiological or computational time lag in the sensordata stream. For example, if a 20-minute time lag is known inherentwithin the continuous analyte sensor, the compensation can be a20-minute projected estimation to provide true present time (or “realtime”) analyte values. Some embodiments can continuously run estimationto compensate for time lag, while other embodiments can perform time lagcompensation estimation only when the user interface (e.g., LCD screen)is activated by a user. Known estimation algorithms and/or the dynamicand intelligent estimation algorithms of the preferred embodiments(e.g., such as described with reference to block 58 and FIGS. 9 to 19)can be used in estimating analyte values herein.

At block 76, the time lag compensation process 70 continuously orperiodically provides output of the present time estimated analytevalues, such as described in more detail above. Output can be sent tothe user interface 20 or to an external device 34.

Referring now to FIG. 7, which is a graph that illustrates the data ofFIG. 5, including reference analyte data, corresponding calibratedsensor analyte data, and corresponding estimated analyte data, showingcompensation for time lag using estimation. The x-axis represents timein minutes and the y-axis represents glucose concentration in mg/dL.Reference glucose values 68 were obtained from the host from thereference glucose monitor during the 4-hour time period and correspondto FIG. 5. Measured glucose data 80 was obtained by smoothing andcalibrating the raw data stream 66 of FIG. 5 using reference glucosevalues 68, such as described in more detail with reference to FIG. 2.Estimated glucose data 82 was obtained by estimating using dynamic andintelligent estimation of the preferred embodiments, which is describedin more detail below.

The measured glucose data 80 has been smoothed and thereby includes adata processing-related time lag, which may be in addition tophysiological or membrane-related time lag, for example. Therefore, themeasured glucose data 80 visibly lags behind the reference glucosevalues 68 on the graph. The estimated glucose data 82 includes dynamicand intelligent estimation of the preferred embodiments in order tocompensate for the time lag, thereby better correlating with thereference glucose values 68. In this embodiment, the time lagcompensation (estimation) is 15 minutes, however in other embodimentsthe time lag compensation (estimation) can be more or less.

In some embodiments, the estimation can be programmed to compensate fora predetermined time lag (for example, 0 to 60 minutes, or more). Insome alternative embodiments, the estimation can be dynamically adjustedbased on a measured time lag; for example, when estimation is used todynamically match sensor analyte data with reference analyte data suchas described below, the time difference between best correspondingsensor analyte data and reference analyte data can be used to determinethe time lag.

FIG. 8 is a flow chart that illustrates the process 84 of matching datapairs from a continuous analyte sensor and a reference analyte sensor inone embodiment. Estimative algorithms of the preferred embodiments areuseful when selectively applied during the process of matchingcorresponding sensor and reference analyte data, for example duringoutlier detection, such as described in more detail with reference toFIG. 2 at block 42, and/or matching data pairs for calibration, such asdescribed in more detail with reference to FIG. 2 at block 44. For thereasons stated above with reference to FIGS. 5 to 7, for example, a timelag associated with the continuous analyte sensor and/or the referenceanalyte monitor can hinder the ability to accurately match data from theanalyte sensor with corresponding data from the reference analytemonitor using time-correspondence only.

At block 86, the data matching process 84 obtains sensor data, which canbe raw, smoothed, and/or otherwise processed. In some embodiments, datamatching can use data from a raw data stream received from an analytesensor, such as described at the block 40. In some embodiments, datamatching can use calibrated data, such as described at the block 50.

At block 88, the data matching process 84, receives analyte values froma reference analyte monitor, including one or more reference glucosedata points, hereinafter referred as “reference data” or “referenceanalyte data.” In an example wherein the analyte sensor is a continuousglucose sensor, the reference analyte monitor can be a self-monitoringblood glucose (SMBG) meter. Other examples are described with referenceto block 42, above.

At block 90, the data matching process 84 estimates one or more analytevalues for a time period during which no data exists (or when data isunreliable or inaccurate, for example) based on the data stream. Forexample, the estimated analyte values can include values at intervalsfrom about 30 seconds to about 5 minutes, and can be estimated for atime period of about 5 minutes to about 60 minutes in the future. Insome embodiments, the time interval and/or time period can be more orless. Known estimation algorithlns and/or the dynamic and intelligentestimation algorithms of the preferred embodiments (e.g., such asdescribed with reference to block 58 and FIGS. 9 to 19) can be used inestimating analyte values herein.

At block 92, the data matching process 84 creates at least one matcheddata pair by matching reference analyte data to a corresponding analytevalue from the one or more estimated analyte values. In someembodiments, the best matched pair can be evaluated by comparing areference data point against individual sensor values over apredetermined time period (for example, ± to 60 minutes). In one suchembodiment, the reference data point is matched with sensor data pointsat intervals (for example, 5-minute intervals of measured historicalanalyte values and estimated future analyte values) and each matchedpair is evaluated. The matched pair with the best correlation (forexample, based on statistical deviation, clinical risk analysis, or thelike) can be selected as the best matched pair and should be used fordata processing. In some alternative embodiments, matching a referencedata point with an average of a plurality of sensor data points over atime period can be used to form a matched pair.

Therefore, the preferred embodiments provide for estimation of analytevalues based on measured analyte values that can be helpful in moreaccurately and/or appropriately matching sensor and reference analytevalues that represent corresponding data. By increasing the accuracy ofmatched data pairs, true real-time estimated analyte values (forexample, without a time lag) can be provided, calibration can beimproved, and outlier detection can be more accurate and convenient,thereby improving overall patient safety and convenience.

While any of the above uses and applications can be applied usingconventional algorithms that provide conventional projection based onfirst or second order regression, for example, it has been found thatanalyte value estimation can be further improved by adaptively applyingalgorithms, for example using dynamic intelligence such as described inmore detail below. The dynamic and intelligent algorithms describedherein can be applied to the uses and applications described above, orfor estimating analyte values at any time for any use or application

FIG. 9 is a flow chart that illustrates the dynamic and intelligentestimation algorithm selection process 96 in one embodiment.

At block 98, the dynamic and intelligent estimation algorithm selectionprocess 96 obtains sensor data, which can be raw, smoothed, and/orotherwise processed. In some embodiments, data matching can use datafrom a raw data stream received from an analyte sensor, such asdescribed at block 40. In some embodiments, data matching can usecalibrated data, such as described at block 50.

At block 100, the dynamic and intelligent estimation algorithm selectionprocess 96 includes selecting one or more algorithms from a plurality ofalgorithms that best fits the measured analyte values. In someembodiments, the estimative algorithm can be selected based onphysiological parameters; for example, in an embodiment wherein theanalyte sensor is a glucose sensor, a first order regression can beselected when the rate of change of the glucose concentration is high,indicating correlation with a straight line, while a second orderregression can be selected when the rate of change of the glucoseconcentration is low, indicating correlation with a curved line. In someembodiments, a first order regression can be selected when the referenceglucose data is within a certain threshold (for example, 100 to 200mg/dL), indicating correlation with a straight line, while a secondorder regression can be selected when the reference glucose data isoutside of a certain threshold (for example, 100 to 200 mg/dL),indicating correlation with a curved line because the likelihood of theglucose concentration turning around (for example, having a curvature)is greatest at high and low values.

Generally, algorithms that estimate analyte values from measured analytevalues include any algorithm that fits the measured analyte values to apattern, and/or extrapolates estimated values for another time period(for example, for a future time period or for a time period during whichdata needs to be replaced). In some embodiments, a polynomial regression(for example, first order, second order, third order, etc.) can be usedto fit measured analyte values to a pattern, and then extrapolated. Insome embodiments, autoregressive algorithms (for example, IIR filter)can be used to fit measured analyte values to a pattern, and thenextrapolated. In some embodiments, measured analyte values can befiltered by frequency before projection (for example, by converting theanalyte values with a Fourier transform, filtering out high frequencynoise, and converting the frequency data back to time values by using aninverse Fourier transform); this data can then be projected forward(extrapolated) along lower frequencies. In some embodiments, measuredanalyte values can be represented with a Wavelet transform (for examplefiltering out specific noise depending on wavelet function), and thenextrapolate forward. In some alternative embodiments, computationalintelligence (for example, neural network-based mapping, fuzzy logicbased pattern matching, genetic-algorithms based pattern matching, orthe like) can be used to fit measured analyte values to a pattern,and/or extrapolate forward. In yet other alternative embodiments,time-series forecasting, using methods such as moving average (single ordouble), exponential smoothing (single, double, or triple), time seriesdecomposition, growth curves, Box-Jenkins, or the like. The plurality ofalgorithms of the preferred embodiments can utilize any one or more ofthe above-described algorithms, or equivalents, in order tointelligently select estimative algorithms and thereby estimate analytevalues

In some embodiments, estimative algorithms further include parametersthat consider external influences, such as insulin therapy, carbohydrateconsumption, or the like. In one such example, these additionalparameters can be user input via the user interface 20 or transmittedfrom an external device 34, such as described in more detail withreference to FIG. 1. By including such external influences in additionalto historical trend data (measured analyte values), analyteconcentration changes can be better anticipated.

At block 102, the selected one or more algorithms are evaluated based onstatistical, clinical, or physiological parameters. In some embodiments,running each algorithm on the data stream tests each of the one or morealgorithms, and the algorithmic result with the best correlation to themeasured analyte values is selected. In some embodiments, thepluralities of algorithms are each compared for best correlation withphysiological parameters (for example, within known or expected rates ofchange, acceleration, concentration, etc). In some embodiments, thepluralities of algorithms are each compared for best fit within aclinical error grid (for example, within “A” region of Clarke ErrorGrid). Although first and second order algorithms are exemplifiedherein, any two or more algorithms such as described in more detailbelow could be programmed and selectively used based on a variety ofconditions, including physiological, clinical, and/or statisticalparameters.

At block 104, the algorithm(s) selected from the evaluation step isemployed to estimate analyte values for a time period. Accordingly,analyte values are more dynamically and intelligently estimated toaccommodate the dynamic nature of physiological data. Additionalprocesses, for example applying physiological boundaries (FIG. 11),evaluation of the estimation algorithms after employing the algorithms(FIG. 13), evaluating a variation of estimated analyte values (FIG. 15),measuring and comparing analyte values (FIG. 18), or the like can beapplied to the dynamic and intelligent estimative algorithms describedwith reference to FIG. 9.

FIG. 10 is a graph that illustrates dynamic and intelligent estimationalgorithm selection applied to a data stream in one embodiment showingfirst order estimation, second order estimation, and the measuredglucose values for the time period, wherein the second order estimationshows a better correlation to the measured glucose data than the firstorder estimation. The x-axis represents time in minutes. The y-axisrepresents glucose concentration in mg/dL.

In the data of FIG. 10, measured (calibrated) sensor glucose data 106was obtained up to time t=0. At t=0, a first order regression 108 wasperformed on the measured data 106 to estimate the upcoming 15-minutetime period. A second order regression 110 was also performed on thedata to estimate the upcoming 15-minute time period. The intelligentestimation of the preferred embodiments, such as described in moredetail below chose the second order regression 110 as the preferredalgorithm for estimation based on programmed conditions (at t=0). Thegraph of FIG. 10 further shows the measured glucose values 112 from t=0to t=15 to illustrate that second order regression 110 does in fact moreaccurately correlate with the measured glucose data 112 than first orderregression 108 from t=0 to t=15.

In the example of FIG. 10, the dynamic and intelligent estimationalgorithm selection determined that the second order regression 110 wasthe preferred algorithm for estimation at t=0 based on conditions. Afirst condition was based on a set threshold that considers second orderregression a better fit when measured glucose values are above 200 mg/dLand trending upwardly. A second condition verifies that the curvature ofthe second order regression line appropriately shows a decelerationabove 200 mg/dL. Although two specific examples of conditions aredescribed herein, dynamic and intelligent estimation can have as many oras few conditions programmed therein as can be imagined or contrived.Some additional examples of conditions for selecting from a plurality ofalgorithms are listed above, however the scope of this aspect of dynamicand intelligent estimation includes any conditional statements that canbe programmed and applied to any algorithms that can be implemented forestimation.

FIG. 11 is a flow chart that illustrates the process 114 of estimatinganalyte values within physiological boundaries in one embodiment. Theembodiment described herein is provided because the estimativealgorithms such as described with reference to FIG. 9 considermathematical equations, which may or may not be sufficient to accuratelyestimate analyte values based on measured analyte values.

At block 116, the analyte value estimation with physiological boundariesprocess 114 obtains sensor data, which can be raw, smoothed, calibratedand/or otherwise processed.

At block 118, the analyte value estimation with physiological boundariesprocess 114 estimates one or more analyte values using one or moreestimation algorithms. In some embodiments, this analyte valueestimation uses conventional projection using first or second orderregression, for example. In some embodiments, dynamically andintelligently selecting of one or more algorithms from a plurality ofalgorithms (FIG. 9), evaluating estimation algorithms after employingthe algorithms (FIG. 13), evaluating a variation of estimated analytevalues (FIG. 15), measuring and comparing analyte values (FIG. 18), orthe like can be applied to the dynamic and intelligent estimativealgorithms described with reference to FIG. 9.

At block 120, the analyte value estimation with physiological boundariesprocess 114 applies physiological boundaries to the estimated analytevalues of block 118. In some circumstances, physiological changes in ahost and associated sensor data stream follow a relatively mathematicalcurvature. However there are additional considerations that are notinherently included in the mathematical calculation of estimativealgorithms, such as physiological boundaries. One example of acircumstance or consideration that can occur is signal noise or signalartifact on the data stream, for example due to transient ischemia,signal from an interfering species, or the like. In such circumstances,normal mathematical calculations can result in estimated analyte valuesthat fall outside of physiological boundaries. For example, a firstorder regression can produce a line that exceeds a known physiologicalrate of change of glucose in humans (for example, about 4 to 5mg/dL/min). As another example, a second order regression can produce acurvature that exceeds a known physiological acceleration in humans (forexample, about 0.1 to 0.2 mg/dL/min²). As yet another example, it hasbeen observed that the best solution for the shape of the curve at anypoint along a glucose signal data stream over a certain time period (forexample, about 20 to 30 minutes) is a straight line, which can be usedto set physiological boundaries. As yet another example, a curvaturedefined by a second order regression at low glucose values (for example,below 80 mg/dL) generally decelerates as it goes down and accelerates asit goes up, while a curvature defined by a second order regression athigh glucose values generally decelerates as it goes up and acceleratesas it goes down. As yet another example, an individual's physiologicalpatterns can be monitored over a time period (for example, from aboutone day to about one year) and individual's physiological patternsquantified using pattern recognition algorithms; the individual'sphysiological patterns could be used to increase the intelligence of theestimation by applying the quantified patterns to the estimated analytevalues.

Accordingly, physiological boundaries, includes those described above,or other measured or known physiological analyte boundaries, cancompliment an estimative algorithm to ensure that the estimated analytevalues fall within known physiological parameters. However, in somealternative embodiments, physiological boundaries can be applied to rawand/or smoothed data, thereby eliminating the need for the estimationstep (block 118).

FIG. 12 is a graph that illustrates physiological boundaries applied toa data stream in one embodiment, wherein the dynamic and intelligentestimation includes performing an estimative algorithm and furtherapplies physiological boundaries to the estimated analyte data. Thex-axis represents time in minutes. The y-axis represents glucoseconcentration in mg/dL. Measured glucose data 122 is shown for about 90minutes up to t=0. At t=0, an estimative algorithm performs estimationusing a second order regression of the previous 40 minutes to generate aslope and acceleration, which are used to extrapolate the estimatedglucose data 124 beginning at the measured analyte data at t=0. At thesame time (t=0), the system uses known physiological parameters todetermine physiologically feasible boundaries of glucose concentrationover the estimated 15-minute period. In this example, the system uses aslope and intercept defined by a first order regression using 25 minutesof data up to t=0, from which the system sets physiological boundariesusing a maximum acceleration of glucose of 0.2 mg/dL/min² and a maximumrate of change of glucose of 4 mg/dL/min for the upcoming 15 minutes.Using the above-described physiological parameters, an upperphysiological boundary 126 and a lower physiological boundary 128 areset. Interestingly, the estimated glucose data 124 falls outside of thephysiological boundaries, namely above the upper physiological boundary126. In this case, the second order regression estimated glucose data124 has either a rate of change greater than 4 mg/dL/min and/oracceleration greater than 0.2 mg/dL/min². Such circumstances can becaused by noise on the signal, artifact of performing regression over apredetermined time period during which a change in analyte concentrationis not best described by a regression line, or numerous other suchaffects.

In this case, estimated glucose values 124 can be adjusted to be theupper limit 126 in order to better represent physiologically feasibleestimated analyte values. In some embodiments, some or all of theestimated analyte values falling outside of the physiological parameterscan trigger the dynamic and intelligent estimative algorithms tore-select an algorithm, or to adjust the parameters of the algorithm(for example, increase and/or decrease the number of data pointsconsidered by the algorithm) to better estimate during that time period.In some alternative embodiments, statistical and or clinical boundariescan be used to bound estimated analyte values and/or adjust theparameters that drive those algorithms.

FIG. 13 is a flow chart that illustrates the process 130 of dynamic andintelligent estimation and evaluation of analyte values in oneembodiment, wherein the estimation algorithms are continuously,periodically, or intermittently evaluated based on statistical,clinical, or physiological parameters to maintain accuracy ofestimation.

At block 132, the dynamic and intelligent estimation and evaluationprocess 130 obtains sensor data, which can be raw, smoothed, calibratedand/or otherwise processed.

At block 134, the dynamic and intelligent estimation and evaluationprocess 130 estimates one or more analyte values using one or moreestimation algorithms. In some embodiments, this analyte valueestimation uses conventional projection using first or second orderregression, for example. In some embodiments, dynamically andintelligently selecting of one or more algorithms from a plurality ofalgorithms (FIG. 9), dynamically and intelligently estimating analytevalues within physiological boundaries (FIG. 11), evaluating a variationof estimated analyte values (FIG. 15), measuring and comparing analytevalues (FIG. 18), or the like can be applied to the dynamic andintelligent estimation and evaluation process described herein withreference to FIG. 13.

The estimative algorithms described elsewhere herein considermathematical equations (FIG. 9) and optionally physiological parameters(FIG. 11), which may or may not be sufficient to accurately estimateanalyte values in some circumstances due to the dynamic nature ofmammalian behavior. For example, in a circumstance where a patient'sglucose concentration is trending upwardly at a constant rate of change(for example, 120 mg/dL at 2 mg/dL/min), an expected physiologicalpattern would likely estimate a continued increase at substantially thesame rate of change over the upcoming approximately 40 minutes, whichwould fall within physiological boundaries. However, if a person withdiabetes were to engage in heavy aerobic exercise, which may not beknown by the estimative algorithm, a slowing of the upward trend, andpossibly a change to a downward trend can possibly result, leading toinaccuracies in the estimated analyte values. Numerous suchcircumstances can occur in the lifestyle of a person with diabetes.However, although analyte values can sometimes be estimated under“normal” circumstances, other circumstances exist that are not “normal”or “expected” and can result in estimative algorithms that produceapparently erroneous results, for example, if they are based solely onmathematical calculations and/or physiological patterns. Accordingly,evaluation of the estimative algorithms can be performed to ensure theaccuracy or quantify a measure of confidence in the estimativealgorithms.

At block 136, the dynamic and intelligent estimation and evaluationprocess 130 evaluates the estimation algorithms employed at block 134 toevaluate a “goodness” of the estimated analyte values. The evaluationprocess performs an evaluation of the measured analyte data with thecorresponding estimated analyte data (e.g., by performing the algorithmon the data stream and comparing the measured with the correspondinganalyte data for a time period). In some embodiments, evaluation can beperformed continually or continuously so that the dynamic andintelligent algorithms are continuously adapting to the changingphysiological analyte data. In some embodiments, the evaluation can beperformed periodically so that the dynamic and intelligent algorithmsare periodically and systematically adapting to the changingphysiological analyte data. In some embodiments, evaluation can beperformed intermittently, for example when an estimative algorithm isinitiated or other such triggers, so that the dynamic and intelligentalgorithms can be evaluated when new or updated data or algorithms arebeing processed.

This evaluation process 130 uses any known evaluation method, forexample based on statistical, clinical, or physiological standards. Oneexample of statistical evaluation is provided below with reference toFIG. 14; however other methods are also possible. In some embodiments,the evaluation process 130 determines a correlation coefficient ofregression. In some embodiments wherein the sensor is a glucose sensor,the evaluation process 130 determines if the selected estimativealgorithm shows that analyte values fall with the “A” and “B” regions ofthe Clarke Error Grid. Other parameters or methods for evaluation areconsidered within the scope of the preferred embodiments. In someembodiments, the evaluation process 130 includes performing a curvatureformula to determine fiducial information about the curvature, whichresults in an evaluation of the amount of noise on the signal.

In some embodiments, the evaluation process 130 calculates physiologicalboundaries to evaluate whether the estimated analyte values fall withinknown physiological constraints. This evaluation is particularly helpfulwhen physiological constraints, such as described with reference to FIG.11 above, have not been applied to the estimative algorithm. In thisembodiment, the estimative algorithm(s) are evaluated to ensure thatthey do not allow estimated analyte values to fall outside ofphysiological boundaries, some examples of which are described in moredetail with reference to FIG. 11 above, and in the definitions section,for example. In some alternative embodiments, clinical or statisticalparameters can be used in a similar manner to bound estimated analytevalues.

If the result of the evaluation is satisfactory (for example, 10%average deviation, correlation coefficient above 0.79, all estimatedanalyte values within A or B region of the Clarke Error Grid, allestimated analyte values within physiological boundaries, or the like),the processing continues to the next step, using the selected estimativealgorithm. However, if the result of the evaluation is unsatisfactory,the process can start the algorithm selection process again, optionallyconsidering additional information, or the processor can determine thatestimation is not appropriate for a certain time period. In onealternative embodiment a signal noise measurement can be evaluated, andif the signal to noise ratio is unacceptable, the processor can modifyits estimative algorithm or other action that can help compensate forsignal noise (e.g., signal artifacts, such as described in co-pendingU.S. application Ser. No. 10/632,537 filed Aug. 22, 2003 and entitled,“SYSTEMS AND METHODS FOR REPLACING SIGNAL ARTIFACTS IN A GLUCOSE SENSORDATA STREAM,” which is incorporated herein by reference in itsentirety).

FIG. 14 is a graph that illustrates an evaluation of the selectedestimative algorithm in one embodiment, wherein a correlation ismeasured to determine a deviation of the measured glucose data with theselected estimative algorithm, if any. The x-axis represents time inminutes. The y-axis represents glucose concentration in mg/dL. Measuredglucose values 140 are shown for about 90 minutes up to t=0. At t=0, theselected algorithm is performed on 40 minutes of the measured glucosevalues 140 up to t=0, which is represented by a regression line 142 inthis embodiment. A data association function is used to determine agoodness of fit of the estimative algorithm on the measured glucose data140; namely, the estimative algorithm is performed retrospectively onthe measured glucose data 140, and is hereinafter referred to asretrospectively estimated glucose data 142 (e.g., estimation prior tot=0), after which a correlation (or deviation) with the measured glucosedata is determined. In this example, the goodness of fit shows a meanabsolute relative difference (MARD) of 3.3% between the measured glucosedata 140 and the retrospectively estimated glucose data 142. While notwishing to be bound to theory, it is believed that this correlation ofthe measured glucose data 140 to the retrospectively estimated glucosedata 142 can be indicative of the correlation of future estimatedglucose data to the measured glucose data for that estimated timeperiod.

Reference is now made to FIG. 15, which is a flow chart that illustratesthe process 150 of analyzing a variation of estimated future analytevalue possibilities in one embodiment. This embodiment takes intoconsideration that analyte values are subject to a variety of externalinfluences, which can cause the measured analyte values to alter fromthe estimated analyte values as the time period that was estimatedpasses. External influences include, but are not limited to, exercise,sickness, consumption of food and alcohol, injections of insulin, othermedications, or the like. For a person with diabetes, for example, evenwhen estimation does not accurately predict the upcoming measuredanalyte values, the estimated analyte values can be valuable to apatient in treatment and in fact can even alter the estimated path byencouraging proactive patient behavior that can cause the patient toavoid the estimated clinical risk. In other words, the deviation ofmeasured analyte values from their corresponding estimated analytevalues may not be an “error” in the estimative algorithm, and is in factone of the benefits of the continuous analyte sensor of the preferredembodiments, namely encouraging patient behavior modification andthereby improving patient health through minimizing clinically riskyanalyte values. Proactive behavior modification (for example, therapiessuch as insulin injections, carbohydrate consumption, exercise, or thelike) can cause the patient's measured glucose to change from theestimated path, and analyzing a variation that can be associated withthe estimated analyte values can encompass many of these changes.Therefore, in addition to estimated analyte values, a variation can becalculated or estimated based on statistical, clinical, and/orphysiological parameters that provides a range of values in which theestimated analyte values can fall.

At block 152, the variation of possible estimated analyte valuesanalysis process 150 obtains sensor data, which can be raw, smoothed,calibrated and/or otherwise processed.

At block 154, the variation of possible estimated analyte valuesanalysis process 150 estimates one or more analyte values using one ormore estimation algorithms. In some embodiments, this analyte valuesestimation uses conventional projection using first or second orderregression, for example. In some embodiments, dynamically andintelligently selecting of one or more algorithms from a plurality ofalgorithms (FIG. 9), dynamically and intelligently estimating analytevalues within physiological boundaries (FIG. 11), dynamic andintelligent estimation and evaluation of estimated analyte values (FIG.13), measuring and comparing analyte values (FIG. 18), or the like canbe applied to the dynamic and intelligent estimation and evaluationprocess described herein with reference to FIG. 15.

At block 156, the variation of possible estimated analyte valuesevaluation process 150 analyzes a variation of the estimated analytedata. Particularly, a statistical, clinical, and/or physiologicalvariation of estimated analyte values can be calculated when applyingthe estimative algorithms and/or can be calculated at regular intervalsto dynamically change as the measured analyte values are obtained. Ingeneral, analysis of trends and their variation allows the estimation ofthe preferred embodiments to dynamically and intelligently anticipateupcoming conditions, by considering internal and external influencesthat can affect analyte concentration.

In some embodiments, physiological boundaries for analytes in mammalscan be used to set the boundaries of variation. For example, knownphysiological boundaries of glucose in humans are discussed in detailherein, with reference to FIG. 11, and in the definitions section,however any physiological parameters for any measured analyte could beimplemented here to provide this variation of physiologically feasibleanalyte value&

In some embodiments, statistical variation can be used to determine avariation of possible analyte values. Statistical variation can be anyknown divergence or change from a point, line, or set of data based onstatistical information. Statistical information includes patterns ordata analysis resulting from experiments, published or unpublished, forexample. In some embodiments, statistical information can include normalpatterns that have been measured statistically in studies of analyteconcentrations in mammals, for example. In some embodiments, statisticalinformation can include errors observed and measured statistically instudies of analyte concentrations in mammals, for example. In someembodiments, statistical information can include predeterminedstatistical standards, for example, deviation less than or equal to 5%on the analyte value. In some embodiments, statistical variation can bea measured or otherwise known signal noise level.

In some embodiments, a variation is determined based on the fact thatthe conventional blood glucose meters are known to have up to a ±20%error in glucose values (namely, on average in the hands of a patient).For example, gross errors in glucose readings are known to occur due topatient error in self-administration of the blood glucose test. In onesuch example, if the user has traces of sugar on his/her finger whileobtaining a blood sample for a glucose concentration test, then themeasured glucose value will likely be much higher than the measuredglucose value in the blood. Additionally, it is known thatself-monitored blood glucose tests (for example, test strips) areoccasionally subject to manufacturing error. In view of this statisticalinformation, in an embodiment wherein a continuous glucose sensor reliesupon a conventional blood glucose meter for calibration, this ±20% errorshould be considered because of the potential for translated effect onthe calibrated sensor analyte data, Accordingly, this exemplaryembodiment would provide for a ±20% variation of estimated glucosevalues based on the above-described statistical information.

In some embodiments, a variation of estimated analyte values can beanalyzed based on individual physiological patterns. Physiologicalpatterns are affected by a combination of at least biologicalmechanisms, physiological boundaries, and external influences such asexercise, sickness, consumption of food and alcohol, injections ofinsulin, other medications, or the like. Advantageously, patternrecognition can be used with continuous analyte sensors to characterizean individual's physiology; for example the metabolism of a person withdiabetes can be individually characterized, which has been difficult toquantify with conventional glucose sensing mechanisms due to the uniquenature of an individual's metabolism. Additionally, this information canbe advantageously linked with external influences (for example, patientbehavior) to better understand the nature of individual humanphysiology, which can be helpful in controlling the basal rate in aperson with diabetes, for example.

While not wishing to be bound to theory, it is believed that monitoringof individual historical physiological analyte data can be used torecognize patterns that can be used to estimate analyte values, orranges of values, in a mammal. For example, measured analyte data for apatient can show certain peaks of glucose levels during a specific timeof day, “normal” AM and PM eating behaviors (for example, that follow apattern), weekday versus weekend glucose patterns, individual maximumrate of change, or the like, that can be quantified usingpatient-dependent pattern recognition algorithms, for example. Patternrecognition algorithms that can be used in this embodiment include, butare not limited to, stochastic nonlinear time-series analysis,exponential (non-linear) autoregressive model, process feedbacknonlinear autoregressive (PFNAR) model, neural networks, or the like.

Accordingly, statistically calculated patterns can provide informationuseful in analyzing a variation of estimated analyte values for apatient that includes consideration of the patient's normalphysiological patterns. Pattern recognition enables the algorithmicanalysis of analyte data to be customized to a user, which is usefulwhen analyte information is variable with each individual user, such ashas been seen in glucose in humans, for example.

In some embodiments, a variation of estimated analyte values is onclinical risk analysis. Estimated analyte values can have higherclinical risk in certain ranges of analyte values, for example analytevalues that are in a clinically risky zone or analyte values that arechanging at a clinically risky rate of change. When a measured analytevalue or an estimated analyte value shows existing or approachingclinical risk, it can be important to analyze the variation of estimatedanalyte values in view of the clinical risk to the patient. For example,in an effort to aid a person with diabetes in avoiding clinically riskyhyper- or hypoglycemia, a variation can be weighted toward theclinically risk zone, which can be used to emphasize the pending dangerto the patient, doctor, or care taker, for example. As another example,the variation of measured or estimated analyte values can be based onvalues that fall within the “A” and/or “B” regions of an error gridAnalysis Method

In case of variation analysis based on clinical risk, the estimatedanalyte values are weighted in view of pending clinical risk. Forexample, if estimated glucose values show a trend toward hypoglycemia ata certain rate of change, a variation of possible trends towardhypoglycemia are weighted to show how quickly the glucose concentrationcould reach 40 mg/dL, for example. As another example, if estimatedglucose values show a trend toward hyperglycemia at a certainacceleration, a variation of possible trends toward hyperglycemia areweighted to show how quickly the glucose concentration could reach 200mg/dL, for example.

In some embodiments, when a variation of the estimated analyte valuesshows higher clinical risk as a possible path within that variationanalysis as compared to the estimated analyte path, the estimatedanalyte values can be adjusted to show the analyte values with the mostclinical risk to a patient. While not wishing to be bound by theory,adjusting the estimated analyte values for the highest variation ofclinical risk exploits the belief that by showing the patient the “worstcase scenario,” the patient is more likely to address the clinical riskand make timely behavioral and therapeutic modifications and/ordecisions that will slow or reverse the approaching clinical risk.

At block 158, the variation of possible estimated analyte valuesevaluation process 150 provides output based on the variation analysis.In some embodiments, the result of this variation analysis provides a“zone” of possible values, which can be displayed to the user,considered in data analysis, and/or used in evaluating of performance ofthe estimation, for example. A few examples of variation analysisdisplay are shown in FIGS. 24 to 26; however other methods of formattingor displaying variation analysis data are contemplated within the scopeof the invention.

FIG. 16 is a graph that illustrates variation analysis of estimatedglucose values in one embodiment, wherein a variation of the estimatedglucose values is analyzed and determined based on known physiologicalparameters. The x-axis represents time in minutes. The y-axis representsglucose concentration in mg/dL. In this embodiment, the known maximumrate of change and acceleration of glucose in humans are used to providethe variation about the estimated glucose path.

The measured glucose values 160 are shown for about 90 minutes up tot=0. At t=0, intelligent and dynamic estimation of the preferredembodiments is performed to obtain estimated glucose values 162. Avariation of estimated glucose values is then determined based onphysiological parameters, including an upper limit 164 and a lower limit166 of variation defined by known physiological parameters, includingrate of change and acceleration of glucose concentration in humans.

FIG. 17 is a graph that illustrates variation of estimated analytevalues in another embodiment, wherein the variation is based onstatistical parameters. The x-axis represents time in minutes and they-axis represents glucose concentration in mg/dL. The measured glucosevalues 170 are shown for about 160 minutes up to t=0. At t=0,intelligent and dynamic estimation of the preferred embodiments isemployed to obtain estimated glucose values 172. A variation is definedby upper and lower limits 174 that were determined using 95% confidenceintervals. Bremer, T.; Gough, D. A. “Is blood glucose predictable fromprevious values? A solicitation for data.” Diabetes 1999, 48, 445-451,which is incorporated by reference herein in its entirety, teaches amethod of determining a confidence interval in one embodiment.

Although some embodiments have been described for a glucose sensor, anymeasured analyte pattern, data analysis resulting from an experiment, orotherwise known statistical information, whether official or unofficial,published or unpublished, proven or anecdotal, or the like, can be usedto provide the statistical variation described herein.

FIG. 18 is a flow chart that illustrates the process 180 of estimating,measuring, and comparing analyte values in one embodiment.

At block 182, the estimating, measuring, and comparing analyte valuesprocess 180 obtains sensor data, which can be raw, smoothed, calibratedand/or otherwise processed.

At block 184, the estimating, measuring, and comparing analyte valuesprocess 180 estimates one or more analyte values for a time period. Insome embodiments, this analyte values estimation uses conventionalprojection using first or second order regression, for example. In someembodiments, dynamically and intelligently selecting of one or morealgorithms from a plurality of algorithms (FIG. 9), dynamically andintelligently estimating analyte values within physiological boundaries(FIG. 11), dynamic and intelligent estimation and evaluation ofestimated analyte values (FIG. 13), variation analysis (FIG. 15), or thelike can be applied to the process described herein with reference toFIG. 18.

At block 186, the estimating, measuring, and comparing analyte valuesprocess 180 obtains sensor data for the time period for which theestimated analyte values were calculated at block 184. In someembodiments, the measured analyte data can be raw, smoothed, calibratedand/or otherwise processed.

At block 188, the estimating, measuring, and comparing analyte valuesprocess 180 compares the estimated analyte data to the measured analytedata for that estimated time period. In general, it can be useful tocompare the estimated analyte data to the measured analyte data for thatestimated time period after estimation of analyte values. Thiscomparison can be performed continuously, namely, at regular intervalsas data streams are processed into measured analyte values.Alternatively, this comparison can be performed based on events, such asduring estimation of measured analyte values, selection of a estimativealgorithm, evaluation of estimative algorithms, variation analysis ofestimated analyte values, calibration and transformation of sensoranalyte data, or the like.

One embodiment is shown in FIG. 19, wherein MARD is used to determine acorrelation (or deviation), if any, between the estimated and measureddata sets. In other embodiments, other methods, such as linearregression, non-linear mapping/regression, rank (for example,non-parametric) correlation, least mean square fit, mean absolutedeviation (MAD), or the like, can be used to compare the estimatedanalyte data to the measured analyte data to determine a correlation (ordeviation), if any.

In one embodiment, wherein estimation is used in outlier detectionand/or in matching data pairs for a continuous glucose sensor (see FIGS.6 and 7), the estimated glucose data can be plotted against referenceglucose data on a clinical error grid (for example, Clarke Error Grid orrate grid) and then compared to the measured glucose data for thatestimated time period plotted against the same reference analyte data onthe same clinical error grid. In alternative embodiments, other clinicalerror analysis methods can be used, such as Consensus Error Grid, rateof change calculation, consensus grid, and standard clinical acceptancetests, for example. The deviation can be quantified by percentdeviation, or can be classified as pass/fail, for example.

In some embodiments, the results of the comparison provide aquantitative deviation value, which can be used to provide a statisticalvariation; for example, if the % deviation is calculated as 8%, then thestatistical variation such as described with reference to FIG. 15 can beupdated with a ±8% variation. In some alternative embodiments, theresults of the comparison can be used to turn on/off the estimativealgorithms, estimative output, or the like. In general, the comparisonproduces a confidence interval (for example, ±8% of estimated values)which can be used in data analysis, output of data to a user, or thelike.

A resulting deviation from this comparison between estimated andcorresponding measured analyte values may or may not imply error in theestimative algorithms. While not wishing to be bound by theory, it isbelieved that the deviation between estimated and corresponding measuredanalyte values is due, at least in part, to behavioral changes by apatient, who observes estimated analyte values and determines to changethe present trend of analyte values by behavioral and/or therapeuticchanges (for example, medication, carbohydrate consumption, exercise,rest, or the like). Accordingly, the deviation can also be used toillustrate positive changes resulting from the educational aspect ofproviding estimated analyte values to the user, which is described inmore detail with reference to FIGS. 20 to 26.

FIG. 19 is a graph that illustrates comparison of estimated analytevalues in one embodiment, wherein previously estimated analyte valuesare compared to time corresponding measured analyte values to determinea correlation (or deviation), if any. The x-axis represents time inminutes. The y-axis represents glucose concentration in mg/dL. Themeasured glucose values 192 are shown for about 105 minutes up to t=15.The estimated analyte values 194, which were estimated at t=0 for 15minutes, are shown superimposed over the measured analyte values 192.Using a 3-point MARD for t=0 to t=15, the estimated analyte values 194can be compared with the measured analyte values 192 to determine a0.55% average deviation.

Input and Output

In general, the above-described estimative algorithms, includingestimation of measured analyte values and variation analysis of theestimated analyte values are useful when provided to a patient, doctor,family member, or the like. Even more, the estimative algorithms areuseful when they are able to provide information helpful in modifying apatient's behavior so that they experience less clinically riskysituations and higher quality of life than may otherwise be possible.Therefore, the above-described data analysis can be output in a varietyof forms useful in caring for the health of a patient

Output can be provided via a user interface, including but not limitedto, visually on a screen, audibly through a speaker, or tactilelythrough a vibrator. Additionally, output can be provided via wired orwireless connection to an external device, including but not limited to,computer, laptop, server, personal digital assistant, modem connection,insulin delivery mechanism, medical device, or other device that can beuseful in interfacing with the receiver.

Output can be continuously provided, or certain output can beselectively provided based on events, analyte concentrations or thelike. For example, an estimated analyte path can be continuouslyprovided to a patient on an LCD screen, while audible alerts can beprovided only during a time of existing or approaching clinical risk toa patient. As another example, estimation can be provided based on eventtriggers (for example, when an analyte concentration is nearing orentering a clinically risky zone). As yet another example, analyzeddeviation of estimated analyte values can be provided when apredetermined level of variation (for example, due to known error orclinical risk) is known.

In contrast to alarms that prompt or alert a patient when a measured orprojected analyte value or rate of change simply passes a predeterminedthreshold, the clinical risk alarms of the preferred embodiments combineintelligent and dynamic estimative algorithms to provide greateraccuracy, more timeliness in pending danger, avoidance of false alarms,and less annoyance for the patient. In general, clinical risk alarms ofthe preferred embodiments include dynamic and intelligent estimativealgorithms based on analyte value, rate of change, acceleration,clinical risk, statistical probabilities, known physiologicalconstraints, and/or individual physiological patterns, thereby providingmore appropriate, clinically safe, and patient-friendly alarms.

In some embodiments, clinical risk alarms can be activated for apredetermined time period to allow for the user to attend to his/hercondition. Additionally, the clinical risk alarms can be de-activatedwhen leaving a clinical risk zone so as not to annoy the patient byrepeated clinical risk alarms, when the patient's condition isimproving.

In some embodiments, the dynamic and intelligent estimation of thepreferred embodiments determines a possibility of the patient avoidingclinical risk, based on the analyte concentration, the rate of change,and other aspects of the dynamic and intelligent estimative algorithmsof the preferred embodiments. If there is minimal or no possibility ofavoiding the clinical risk, a clinical risk alarm will be triggered.However, if there is a possibility of avoiding the clinical risk, thesystem can wait a predetermined amount of time and re-analyze thepossibility of avoiding the clinical risk. In some embodiments, whenthere is a possibility of avoiding the clinical risk, the system willfurther provide targets, therapy recommendations, or other informationthat can aid the patient in proactively avoiding the clinical risk.

In some embodiments, a variety of different display methods are used,such as described in the preferred embodiments, which can be toggledthrough or selectively displayed to the user based on conditions or byselecting a button, for example. As one example, a simple screen can benormally shown that provides an overview of analyte data, for examplepresent analyte value and directional trend. More complex screens canthen be selected when a user desired more detailed information, forexample, historical analyte data, alarms, clinical risk zones, or thelike.

FIG. 20 is an illustration of the receiver in one embodiment showing ananalyte trend graph, including measured analyte values, estimatedanalyte values, and a clinical risk zone. The receiver 12 includes anLCD screen 30, buttons 32, and a speaker 24 and/or microphone. Thescreen 30 displays a trend graph in the form of a line representing thehistorical trend of a patient's analyte concentration. Although axes mayor may not be shown on the screen 30, it is understood that atheoretical x-axis represents time and a theoretical y-axis representsanalyte concentration.

In some embodiments such as shown in FIG. 20, the screen showsthresholds, including a high threshold 200 and a low threshold 202,which represent boundaries between clinically safe and clinically riskyconditions for the patients. In one exemplary embodiment, a normalglucose threshold for a glucose sensor is set between about 100 and 160mg/dL, and the clinical risk zones 204 are illustrated outside of thesethresholds. In alternative embodiments, the normal glucose threshold isbetween about 80 and about 200 mg/dL, between about 55 and about 220mg/dL, or other threshold that can be set by the manufacturer,physician, patient, computer program, or the like. Although a fewexamples of glucose thresholds are given for a glucose sensor, thesetting of any analyte threshold is not limited by the preferredembodiments.

In some embodiments, the screen 30 shows clinical risk zones 204, alsoreferred to as danger zones, through shading, gradients, or othergraphical illustrations that indicate areas of increasing clinical risk.Clinical risk zones 204 can be set by a manufacturer, customized by adoctor, and/or set by a user via buttons 32, for example. In someembodiments, the danger zone 204 can be continuously shown on the screen30, or the danger zone can appear when the measured and/or estimatedanalyte values fall into the danger zone 204. Additional informationthat can be displayed on the screen, such as an estimated time toclinical risk. In some embodiments, the danger zone can be divided intolevels of danger (for example, low, medium, and high) and/or can becolor-coded (for example, yellow, orange, and red) or otherwiseillustrated to indicate the level of danger to the patient.Additionally, the screen or portion of the screen can dynamically changecolors or illustrations that represent a nearness to the clinical riskand/or a severity of clinical risk.

In some embodiments, such as shown in FIG. 20, the screen 30 displays atrend graph of measured analyte data 206. Measured analyte data can besmoothed and calibrated such as described in more detail elsewhereherein. Measured analyte data can be displayed for a certain time period(for example, previous 1 hour, 3 hours, 9 hours, etc.) In someembodiments, the user can toggle through screens using buttons 32 toview the measured analyte data for different time periods, usingdifferent formats, or to view certain analyte values (for example, highsand lows).

In some embodiments such as shown in FIG. 20, the screen 30 displaysestimated analyte data 208 using dots. In this illustration, the size ofthe dots can represent the confidence of the estimation, a variation ofestimated values, or the like. For example, as the time gets fartheraway from the present (t=0) the confidence level in the accuracy of theestimation can decline as is appreciated by one skilled in the art. Insome alternative embodiments, dashed lines, symbols, icons, or the likecan be used to represent the estimated analyte values. In somealternative embodiments, shaded regions, colors, patterns, or the likecan also be used to represent the estimated analyte values, a confidencein those values, and/or a variation of those values, such as describedin more detail in preferred embodiments.

Axes, including time and analyte concentration values, can be providedon the screen, however are not required. While not wishing to be boundby theory, it is believed that trend information, thresholds, and dangerzones provide sufficient information to represent analyte concentrationand clinically educate the user. In some embodiments, time can berepresented by symbols, such as a sun and moon to represent day andnight. In some embodiments, the present or most recent measured analyteconcentration, from the continuous sensor and/or from the referenceanalyte monitor can be continually, intermittently, or selectivelydisplayed on the screen.

The estimated analyte values 208 of FIG. 20 include a portion, whichextends into the danger zone 204. By providing data in a format thatemphasizes the possibility of clinical risk to the patient, appropriateaction can be taken by the user (for example, patient or caretaker) andclinical risk can be preempted.

FIG. 21 is an illustration of the receiver in another embodiment showinga representation of analyte concentration and directional trend using agradient bar. In this embodiment, the screen illustrates the measuredanalyte values and estimated analyte values in a simple but effectivemanner that communicates valuable analyte information to the user

In this embodiment, a gradient bar 210 is provided that includesthresholds 212 set at high and lows such as described in more detailwith reference to FIG. 20, above. Additionally, colors, shading, orother graphical illustration can be present to represent danger zones214 on the gradient bar 210 such as described in more detail withreference to FIG. 20, above.

The measured analyte value is represented on the gradient bar 210 by amarker 216, such as a darkened or colored bar. By representing themeasured analyte value with a bar 216, a low-resolution analyte value ispresented to the user (for example, within a range of values). Forexample, each segment on the gradient bar 210 can represent about 10mg/dL of glucose concentration. As another example, each segment candynamically represent the range of values that fall within the “A” and“B” regions of the Clarke Error Grid. While not wishing to be bound bytheory, it is believe that inaccuracies known both in reference analytemonitors and/or continuous analyte sensors are likely due to knownvariables such as described in more detail elsewhere herein, and can bede-emphasized such that a user focuses on proactive care of thecondition, rather than inconsequential discrepancies within and betweenreference analyte monitors and continuous analyte sensors.

Additionally, the representative gradient bar communicates thedirectional trend of the analyte concentration to the user in a simpleand effective manner, namely by a directional arrow 218. For example, inconventional diabetic blood glucose monitoring, a person with diabetesobtains a blood sample and measures the glucose concentration using atest strip, or the like. Unfortunately, this information does not tellthe person with diabetes whether the blood glucose concentration isrising or falling. Rising or falling directional trend information canbe particularly important in a situation such as illustrated in FIG. 21,wherein if the user does not know that the glucose concentration isrising, he/she may assume that the glucose concentration is falling andnot attend to his/her condition. However, because rising directionaltrend information 218 is provided, the person with diabetes can preemptthe clinical risk by attending to his/her condition (for example,administer insulin). Estimated analyte data can be incorporated into thedirectional trend information by characteristics of the arrow, forexample, size, color, flash speed, or the like.

In some embodiments, the gradient bar can be a vertical instead ofhorizontal bar. In some embodiments, a gradient fill can be used torepresent analyte concentration, variation, or clinical risk, forexample. In some embodiments, the bar graph includes color, for examplethe center can be green in the safe zone that graduates to red in thedanger zones; this can be in addition to or in place of the dividedsegments. In some embodiments, the segments of the bar graph are clearlydivided by lines; however color, gradation, or the like can be used torepresent areas of the bar graph. In some embodiments, the directionalarrow can be represented by a cascading level of arrows to a representslow or rapid rate of change. In some embodiments, the directional arrowcan be flashing to represent movement or pending danger.

The screen 30 of FIG. 21 can further comprise a numerical representationof analyte concentration, date, time, or other information to becommunicated to the patient. However, a user can advantageouslyextrapolate information helpful for his/her condition using the simpleand effective representation of this embodiment shown in FIG. 21,without reading a numeric representation of his/her analyteconcentration.

In some alternative embodiments, a trend graph or gradient bar, a dial,pie chart, or other visual representation can provide analyte data usingshading, colors, patterns, icons, animation, or the like.

FIG. 22 is an illustration of a receiver in one embodiment, whichincludes measured analyte values and a target analyte value(s). FIG. 23is an illustration of the receiver of 22 further including estimatedanalyte values. FIG. 24 is an illustration of the receiver of 23 furtherincluding variations of estimated analyte values and including therapyrecommendations to aid a user in obtaining the target analyte value.

FIG. 22 is an illustration of the receiver 12 in one embodiment, whereinthe screen 30 shows measured analyte values 220 and one (or more)clinically acceptable target analyte values 222. The measured analytevalues 220 are illustrated as a trend graph, such as described withreference to FIG. 20, however other representations are also possible.

Additionally, one or more clinically acceptable target analyte values222 are provided as output, for example such as shown in FIG. 22. Insome embodiments, the clinically acceptable target analyte values can beobtained from a variation analysis of clinical, physiological, orstatistical variation, such as described in more detail elsewhereherein. Namely, the variation analysis provides the analyzed variationof the estimated analyte values, and the output module 18 (or processor16) further analyzes the variation of estimated analyte values for thosethat are clinically acceptable and optionally also ensures physiologicalfeasibility. For example, analysis of clinical risk can visually directa patient to aim for an analyte value in a safe zone (for example,outside of the clinically risky zone).

In some embodiments, the output displays a point representing a targetanalyte value. In some embodiments, the output displays an objectrepresenting a general target analyte area. In some embodiments, theoutput displays a path of target analyte values. In some embodiments,the output displays a range of target analyte values along that path.

Humans are generally particularly responsive to targets, namely, able tounderstand the intention of targets and desire to obtain them.Advantageously, the output of target analyte values provides a goaltowards which the user will aim. In the example shown on FIG. 20, themeasured analyte values 220 indicate an upward trend of analyteconcentration, and a user can likely visualize that the trend of themeasured analyte values 220 will not likely hit the target 222 withoutintervention or action. Therefore, a user will be prompted toproactively care for his/her analyte concentration in an effort to hitthe target analyte value(s) 222 (for example, administer insulin).

In some embodiments, the manufacturer, physician, patient, computerprogram, or the like can set the target analyte values. In someembodiments, a physician can set static target analyte values based onage, time of day, meal time, severity of medical condition, or the like;in such embodiments, the targets can be regularly or intermittentlydisplayed in an effort to modify patient behavior through habitualreminders and training. Targets can be continually maintained on thescreen or selectively displayed, for example when clinical risk isestimated, but can be avoided. In some embodiments, the target valuescan be dynamic targets, namely, targets that are dependent upon variableparameters such as age, time of day, meal time, severity of medicalcondition, medications received (for example, insulin injections) or thelike, which can be input by a user or external device. In one example oftargets useful for a person with diabetes monitoring glucoseconcentration, the target glucose levels for a person with diabetes aretypically between about 80 and about 130 mg/dL before meals and lessthan about 180 mg/dL one to two hours after a meal. In another exemplaryembodiment, the amount and timing of insulin injections can beconsidered in determining the estimation of and target glucose rangesfor a person with diabetes.

FIG. 23 is an illustration of the receiver 12 in another embodimentshowing the measured analyte values 220 and clinically acceptable targetanalyte value(s) 222 of FIG. 22 and further showing estimated analytevalues 224 on the same screen. In some embodiments, the data can beseparated onto different screens that can be selectively viewed.However, viewing both estimated analyte values and the target analytevalues can be useful in educating the patient regarding control ofhis/her analyte levels, since estimated and target analyte values arephysiologically feasible in view of known physiological parametersdescribed elsewhere herein. Estimated analyte values can be calculatedand displayed in any manner described in the preferred embodiments.

FIG. 24 is an illustration of a receiver in another embodiment,including measured analyte values 220, target analyte values 222,estimated analyte values 224, such as described in more detail abovewith reference to FIGS. 22 and 23, and further including variations ofestimated analyte values 226 and therapy recommendations 228 on thescreen to help the user obtain the displayed target analyte values 222.The variations of estimated analyte values are calculated such asdescribed in more detail with reference to FIG. 15.

The target analyte values presented should be physiologically feasible;therefore, type and/or amount of therapy can be determined (orestimated) to aid the patient in obtaining those therapy goals. In someembodiments, the therapy recommendations are representative icons, suchas the injection icon 228 shown in FIG. 24. In alternative embodiments,icons can include an apple, orange juice, candy bar, or any iconrepresentative of eating, drinking, or medicating, for example. In someembodiments, the therapy recommendations are preset alphanumericmessages (for example, “consume carbohydrates”, “inject insulin”, or “notherapy required”). In some embodiments therapy recommendations can becustomized (for example, by a manufacturer, physician, patient, computerprogram, and/or the like) in order to provide more reliable, accurate,clinically safe, and/or individualized goals. For example, a physiciancan input information helpful in determining therapy recommendationsusing individual physiological considerations. As another example, datacan be input via the user interface or via a wired or wirelessconnection to the receiver, such as age, time of day, meal time,severity of medical condition, medications received (for example,insulin injections) or the like, which can be used to determine theappropriate therapy recommendations.

In some embodiments, the therapy recommendations include a variety ofscenarios, which the viewer can view and/or select. In theseembodiments, the patient is given more control and able to makedecisions based that fits best with their lifestyle or presentcircumstance, or considering external influences of which the system wasunaware.

In some embodiments, therapy recommendations are sent to an externaldevice (for example, insulin delivery mechanism), which is described inmore detail with reference to FIGS. 27 to 30.

FIGS. 25 and 26 are views of the receiver showing an analyte trendgraph, including measured analyte values and dynamic visualrepresentation of range of estimated analyte values based on a variationanalysis, such as described in more detail with reference to FIG. 15.

FIG. 25 is an illustration of a receiver 12 in another embodiment,including a screen 30 that shows the measured analyte values 230 and avariation of estimated analyte values 232 in one exemplary embodiment.In this embodiment, the visual representation of the variation ofestimated analyte values 232 includes exemplary paths representative ofthe analyzed variation of estimated analyte values that illustrates arange of possible future analyte values. In some embodiments, thevariation of estimated analyte values 232 is represented by a shape thatbegins at the most recently measured analyte value 234 and includesboundaries 236 that represent the range of possible variations ofestimated analyte values for a future time period. The shape can bestatic or dynamic depending on the type of variation analyzed by theestimative algorithm, for example a fan, teardrop, or other shapedobject.

FIG. 26 is an illustration of a receiver 12 in another embodiment,including a screen 30 that shows the measured analyte values 238 and avariation of estimated analyte values 240 in another exemplaryembodiment. In this embodiment, the variation can include an estimatedpath and boundaries, for example, which can be obtained from a variationanalysis and/or from physiological parameters, for example. In somealternative embodiments, color or other illustrative representation oflevels of safety or danger can be provided on the screen.

FIG. 27 is an illustration of a receiver 12 in another embodiment,including a screen 30 that shows a numerical representation of the mostrecent measured analyte value 242. This numerical value 242 ispreferably a calibrated analyte value, such as described in more detailwith reference to FIG. 2. Additionally, this embodiment preferablyprovides an arrow 244 on the screen 30, which represents the rate ofchange of the host's analyte concentration. A bold “up” arrow is shownon the drawing, which preferably represents a relatively quicklyincreasing rate of change. The arrows shown with dotted lines illustrateexamples of other directional arrows (for example, rotated by 45degrees), which can be useful on the screen to represent various otherpositive and negative rates of change. Although the directional arrowsshown have a relative low resolution (45 degrees of accuracy), otherarrows can be rotated with a high resolution of accuracy (for exampleone degree of accuracy) to more accurately represent the rate of changeof the host's analyte concentration. In some alternative embodiments,the screen provides an indication of the acceleration of the host'sanalyte concentration.

A second numerical value 246 is shown, which is representative of avariation of the measured analyte value 242. The second numerical valueis preferable determined from a variation analysis based on statistical,clinical, or physiological parameters, such as described in more detailelsewhere herein. In one embodiment, the second numerical value 246 isdetermined based on clinical risk (for example, weighted for thegreatest possible clinical risk to a patient). In another embodiment,the second numerical representation 246 is an estimated analyte valueextrapolated to compensate for a time lag, such as described in moredetail elsewhere herein. In some alternative embodiments, the receiverdisplays a range of numerical analyte values that best represents thehost's estimated analyte value (for example, ±10%). In some embodiments,the range is weighted based on clinical risk to the patient. In someembodiments, the range is representative of a confidence in theestimated analyte value and/or a variation of those values. In someembodiments, the range is adjustable.

Patient Display

The potential of continuous glucose monitoring as an aid to bothdiabetic patients and their caregivers is well recognized. For thepatient, continuous monitoring provides hour-to-hour glucose informationthat enables intensive therapy: it can be used to reduce the extent ofhyperglycemic excursions without increasing the risk of hypoglycemicevents. For caregivers of patients with diabetes, continuous monitoringprovides day-to-day glucose information that can be used to optimizetherapy. Despite these differences in purpose/perspective (hour-to-hourdata for the patient, day-to-day information for the caregiver), theconventional display of continuous glucose data has heretofore not beenadapted to the intended use/user. Accordingly, continuous glucosedisplay methods that are utility-driven, and that allow the data to beeasily perceived and interpreted is desirable.

Glucose data are typically displayed on a graph with y-axis that spans aphysiologic range of glucose (e.g. 40-400 mg/dl) and is uniform, i.e.the distance on the graph between 60 and 80 mg/dl is the same as thedistance between 160 and 180 mg/dl, even though the clinical meanings ofthese two differences are significantly different. An alternativedisplay uses a non-uniform y-axis that makes differences at low glucoselevels easier to perceive. The difference in appearance of these twographs is depicted in FIG. 28, which illustrates the conventionaldisplay of a 9-hour trend graph; FIG. 29 illustrates a display with ay-axis that has been equally divided into three zones (low, medium, andhigh glucose) though the glucose range (max-min) of each zone isdifferent (40-90 mg/dl, 90-180 mg/dl, 180-400 mg/dl). The non-uniformy-axis in FIG. 29 appears to cause distortion to the glucose trend butdoes not appear to be misleading. More importantly, the dynamics at lowglucose are more easily perceived in FIG. 29 than in FIG. 28.

Physicians use continuous glucose monitoring primarily for therapyoptimization. Though the hour-to-hour dynamics of glucose can containinformation related to therapy adjustment, a longer-term/summaryperspective is perhaps easier perceive and interpret, and morereflective of changes in a patient's glycemic control. In this way,physician monitoring of a patient's glycemic control is similar toprocess monitoring used in quality control of manufactured products: theaim of both is to rapidly detect when the system/process is in or out ofcontrol, or to detect trends that can indicate changes in control.Control charts, which plot averages and ranges of process parametersover time, are a well-established and powerful illustration of processcontrol and can be applicable to continuous glucose monitoring. FIGS. 30and 31 illustrate the difference in how well the data reflect changes inglycemic control. FIG. 30 is a conventional plot of glucose over oneweek; FIG. 31 is a plot of the 24-hour (12 AM-12 AM) median(±interquartile range) glucose.

The display provides improved utility of continuous glucose data,enabling improved clinical outcomes, and offers advantages over priorart displays wherein the display of continuous glucose data is nottailored to the intended use.

FIG. 32 is an illustration of a receiver that interfaces with acomputer. A receiver 12 is provided that is capable of communicationwith a computer 280. The communication can include one-way or two-waywired or wireless transmissions 282. The computer 280 can be any systemthat processes information, such as a PC, server, personal digitalassistant (PDA), or the like.

In some embodiments, the receiver sends information to the computer, forexample, measured analyte data, estimated analyte data, target analytedata, therapy recommendations, or the like. The computer can includesoftware that processes the data in any manner known in the art.

In some embodiments, the computer sends information to the receiver; forexample, updating software, customizing the receiver programming (forexample, setting individualized parameters), providing real timeinformation (for example, mealtime and exercise that has been enteredinto a PDA), or the like.

FIG. 33 is an illustration of a receiver 12 that interfaces with a modem290, wherein data is transmitted via wireless transmissions 292 betweenthe receiver and a modem in order to interface with a telecommunicationsline (for example, phone, pager, internet, network, etc). By providingan interface with a telecommunications line, the receiver can send andreceive information from parties remote from the receiver, such as at ahospital, doctor's office, caretaker's computer, nationally-basedserver, or the like.

In some embodiments, the modem allows the receiver to send emergencymessages to an emergency contact, such as a family member, hospital,Public Safety Answering Point (PSAP), or the like when analyteconcentration are in a zone of extreme clinical risk. In someembodiments, a patient's doctor monitors his/her analyte concentrationremotely and is able to request an appointment when certain conditionsare not being met with the patient's analyte concentration. Numerousother uses can be contrived for communicating information via a modem290 between the receiver 12 and another party, all of which areencompassed in the preferred embodiments.

FIG. 34 is an illustration of a receiver 12 that interfaces with aninsulin pen 300, wherein data is transmitted via wireless transmission302 between the receiver and the insulin pen 300. In some embodiments,the receiver sends therapy recommendations to the insulin pen, such asamount and time of insulin injection. In some embodiments, the insulinpen sends amount of therapy administered by a patient, such as type,amount, and time of administration. Such information can be used in dataanalysis, including estimation of analyte values, output of therapyrecommendations, and trend analysis, for example.

FIG. 35 is an illustration of a receiver 12 that interfaces with aninsulin pump 310, wherein data is transmitted via wireless transmission312 between the receiver 12 and the insulin pump 310. In someembodiments, the receiver sends therapy recommendations to the insulinpump 310, such as amount and time of insulin administration. In someembodiments, the insulin pump 310 sends information regarding therapy tobe administered such as type, amount, and time of administration. Suchinformation can be used in data analysis, including estimation ofanalyte values, output of therapy recommendations, and trend analysis,for example.

In general, any of the above methods of data input and output can becombined, modified, selectively viewed, selectively applied, orotherwise altered without departing from the scope of the presentinvention.

Methods and devices that can be suitable for use in conjunction withaspects of the preferred embodiments are disclosed in copendingapplications including U.S. application Ser. No. 10/695,636 filed Oct.28, 2003 and entitled, “SILICONE COMPOSITION FOR BIOCOMPATIBLEMEMBRANE”; U.S. application Ser. No. 10/632,537 filed Aug. 22, 2003 andentitled, “SYSTEMS AND METHODS FOR REPLACING SIGNAL ARTIFACTS IN AGLUCOSE SENSOR DATA STREAM”; U.S. application Ser. No. 10/646,333 filedAug. 22, 2003 entitled, “OPTIMIZED SENSOR GEOMETRY FOR AN IMPLANTABLEGLUCOSE SENSOR”; U.S. application Ser. No. 10/647,065 filed Aug. 22,2003 entitled, “POROUS MEMBRANES FOR USE WITH IMPLANTABLE DEVICES”; U.S.application Ser. No. 10/633,367 filed Aug. 1, 2003 entitled, “SYSTEM ANDMETHODS FOR PROCESSING ANALYTE SENSOR DATA”; U.S. application Ser. No.09/916,386 filed Jul. 27, 2001 and entitled “MEMBRANE FOR USE WITHIMPLANTABLE DEVICES”; U.S. application Ser. No. 09/916,711 filed Jul.27, 2001 and entitled “SENSOR HEAD FOR USE WITH IMPLANTABLE DEVICE”;U.S. application Ser. No. 09/447,227 filed Nov. 22, 1999 and entitled“DEVICE AND METHOD FOR DETERMINING ANALYTE LEVELS”; U.S. applicationSer. No. 10/153,356 filed May 22, 2002 and entitled “TECHNIQUES TOIMPROVE POLYURETHANE MEMBRANES FOR IMPLANTABLE GLUCOSE SENSORS”; U.S.application Ser. No. 09/489,588 filed Jan. 21, 2000 and entitled “DEVICEAND METHOD FOR DETERMINING ANALYTE LEVELS”; U.S. application Ser. No.09/636,369 filed Aug. 11, 2000 and entitled “SYSTEMS AND METHODS FORREMOTE MONITORING AND MODULATION OF MEDICAL DEVICES”; and U.S.application Ser. No. 09/916,858 filed Jul. 27, 2001 and entitled “DEVICEAND METHOD FOR DETERMINING ANALYTE LEVELS,” as well as issued patentsincluding U.S. Pat. No. 6,001,067 issued Dec. 14, 1999 and entitled“DEVICE AND METHOD FOR DETERMINING ANALYTE LEVELS”; U.S. Pat. No.4,994,167 issued Feb. 19, 1991 and entitled “BIOLOGICAL FLUID MEASURINGDEVICE”; and U.S. Pat. No. 4,757,022 filed Jul. 12, 1988 and entitled“BIOLOGICAL FLUID MEASURING DEVICE.” All of the above patents and patentapplications are incorporated in their entirety herein by reference.

The above description provides several methods and materials of theinvention. This invention is susceptible to modifications in the methodsand materials, as well as alterations in the fabrication methods andequipment. Such modifications will become apparent to those skilled inthe art from a consideration of this application or practice of theinvention provided herein. Consequently, it is not intended that thisinvention be limited to the specific embodiments provided herein, butthat it cover all modifications and alternatives coming within the truescope and spirit of the invention as embodied in the attached claims.All patents, applications, and other references cited herein are herebyincorporated by reference in their entirety.

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification and claims are to be understoodas being modified in all instances by the term “about.” Accordingly,unless indicated to the contrary, the numerical parameters set forth inthe specification and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by thepresent invention. At the very least, and not as an attempt to limit theapplication of the doctrine of equivalents to the scope of the claims,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

1. A method for processing sensor data, the method comprising: receivingsensor data from a continuous analyte sensor; receiving reference datafrom a reference analyte monitor measured at a reference time period;estimating at least one analyte value based on the sensor data for thereference time period; and forming at least one matched data pair bymatching the reference data to the at least one estimated analyte value.2. The method of claim 1, further comprising a step of performingoutlier detection based at least in part on the at least one matcheddata pair.
 3. The method of claim 1, further comprising a step ofcalibrating the sensor data based at least in part on the at least onematched data pair.
 4. The method of claim 1, further comprisingreceiving sensor data for the reference time period and comparing theestimated analyte value for the reference time period with sensor datafor the reference time period to determine a time lag between theestimated analyte value and the time-corresponding analyte value.
 5. Themethod of claim 1, wherein the step of estimating at least one analytevalue based on the sensor data for the reference time period includescompensating for a time lag between the reference data and the sensordata.
 6. The method of claim 1, wherein the step of estimating at leastone analyte value based on the sensor data for the reference time periodcomprises estimating one or more analyte values for a time period duringwhich no sensor data exists.
 7. The method of claim 6, wherein the stepof estimating one or more analyte values for a time period during whichno sensor data exists comprises estimating one or more analyte valuesfor a future time period.
 8. The method of claim 6, wherein the step ofestimating one or more analyte values for a future time period isconfigured to compensate for a time lag.
 9. The method of claim 1,wherein the step of estimating at least one analyte value based on thesensor data for the reference time period comprises estimating one ormore analyte values for a time period when data is determined to beunreliable and/or inaccurate.
 10. The method of claim 1, wherein thestep of forming at least one matched data pair by matching the referencedata to the estimated analyte value comprises forming a plurality ofmatched data pairs by matching a plurality a reference data points witha plurality of sensor data points over a predetermined time period. 11.The method of claim 10, further comprising the step of determining abest matched pair by evaluating the plurality of matched data pairs. 12.The method of claim 11, wherein the step of evaluating the plurality ofmatched data pairs is based at least in part on a statistical deviationand/or clinical risk analysis.
 13. A system for processing sensor data,the system comprising: a sensor input module operatively connected to acontinuous analyte sensor that receives sensor data from the continuousanalyte sensor; a reference input module that receives reference datafrom a reference analyte monitor; and a processor module comprisingprogramming configured to estimate at least one analyte value based onthe sensor data for the reference time period, wherein the processormodule further comprises programming configured to form at least onematched data pair by matching the reference data to the at least oneestimated analyte value.
 14. The system of claim 13, wherein theprocessor module further comprises programming configured to performoutlier detection based at least in part on the at least one matcheddata pair.
 15. The system of claim 13, wherein the processor modulefurther comprises programming configured to calibrate the sensor databased at least in part on the at least one matched data pair.
 16. Thesystem of claim 13, wherein the sensor input module further receivessensor data for the reference time period, and wherein the processormodule further comprises programming configured to compare the estimatedanalyte value for the reference time period with sensor data for thereference time period to determine a time lag between the estimatedanalyte value and the time-corresponding analyte value.
 17. The systemof claim 13, wherein the programming configured to estimate at least oneanalyte value based on the sensor data for the reference time periodcompensates for a time lag between the reference data and the sensordata.
 18. The system of claim 13, wherein the programming configured toestimate at least one analyte value based on the sensor data for thereference time period estimates one or more analyte values for a timeperiod during which no sensor data exists.
 19. The system of claim 18,wherein the programming configured to estimate one or more analytevalues for a time period during which no sensor data exists estimatesone or more analyte values for a future time period.
 20. The system ofclaim 18, wherein the programming configured to estimate one or moreanalyte values for a future time period is configured to compensate fora time lag.
 21. The system of claim 13, wherein the programmingconfigured to estimate at least one analyte value based on the sensordata for the reference time period estimates one or more analyte valuesfor a time period when data is determined to be unreliable and/orinaccurate.
 22. The system of claim 13, wherein the programmingconfigured to form at least one matched data pair by matching thereference data to the estimated analyte value forms a plurality ofmatched data pairs by matching a plurality a reference sensor data pointwith a plurality of sensor data points over a predetermined time period.23. The system of claim 22, wherein the processor module furthercomprises programming to determine a best matched pair by evaluating theplurality of matched data pairs.
 24. The system of claim 23, wherein theprogramming to determine a best matched pair by evaluating the pluralityof matched data pairs is based at least in part on a statisticaldeviation and/or clinical risk analysis.